Forward-looking transactive pricing schemes for use in a market-based resource allocation system

ABSTRACT

Disclosed herein are representative embodiments of methods, apparatus, and systems for distributing a resource (such as electricity) using a resource allocation system. One of the disclosed embodiments is a method for generating a bid value for purchasing electricity in a market-based resource allocation system. In this embodiment, a desired performance value indicative of a user&#39;s desired performance level for an electrical device is received. Price information from an electricity futures market is received. A bid value for purchasing electricity from a local resource allocation market sufficient to operate the electrical device at the desired performance level is computed. In this embodiment, the computing is performed based at least in part on the desired performance value and based at least in part on the price information from the electricity futures market.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.13/096,682, filed Apr. 28, 2011, which is hereby incorporated byreference.

FIELD

This application relates generally to the field of power grid managementand control.

BACKGROUND

Demand response and dynamic pricing programs are expected to playincreasing roles in the modern smart grid environment. These programstypically utilize a price signal as a means to control demand. Activemarkets allow customers to respond to fluctuations in wholesaleelectrical costs, but may not allow the utility to directly andcompletely control demand. Transactive markets, utilizing distributedtransactive controllers and a centralized auction, can provide aninteractive system that helps ensure that consumer demand is met, supplylimits are not exceeded, and that price volatility is reduced. With thecurrent proliferation of computing and communication resources, theability now exists to create transactive demand response programs at theresidential level.

SUMMARY

Disclosed below are representative embodiments of methods, apparatus,and systems for distributing a resource (such as electricity). Forexample, improved systems and methods for computing supply and demandbids in a transactive and active market are described herein. Inparticular embodiments, price information from an electricity futuresmarket (e.g., the day-ahead market) is used in determining the bid.

One of the disclosed embodiments is a method for generating a bid valuefor purchasing electricity in a market-based resource allocation system.In this embodiment, a desired performance value indicative of a user'sdesired performance level for an electrical device is received. Priceinformation from an electricity futures market is received. A bid valuefor purchasing electricity from a local resource allocation marketsufficient to operate the electrical device at the desired performancelevel is computed. In this embodiment, the computing is performed basedat least in part on the desired performance value and based at least inpart on the price information from the electricity futures market. Thebid value is transmitted to a computer that operates the local resourceallocation market. In some implementations, the electricity futuresmarket is a day-ahead electricity market. In certain implementations,one or more user tolerance values indicative of the user's willingnessto tolerate variations from the desired performance level are received,and the bid value is additionally based at least in part on the one ormore user tolerance values. In some implementations, the electricaldevice is one of an air-conditioning unit, heating unit, hot waterheater, or refrigerator; and the one or more user tolerance valuesindicate a lower temperature limit, an upper temperature limit, or botha lower temperature limit and an upper temperature limit. In certainimplementations, the price information from the electricity futuresmarket comprises cleared prices from the electricity futures market, andthe computing of the bid value is performed using an average of thecleared prices from the electricity futures market and a standarddeviation of the cleared prices from the electricity futures market.Such implementations can further comprise computing the average and thestandard deviation with the computing hardware or receiving the averageand the standard deviation from a remote source. In someimplementations, the bid value is additionally based at least in part onprice information from the local resource allocation market. Forexample, the computing of the bid value can be performed using one ormore weighted sums computed from the price information from theelectricity futures market and from the price information for the localresource allocation market. Further, one or more of the weighted sumscan be controlled by a variable weighting factor that varies in responseto one or more of the time of day or current demand in the localresource allocation market. Further, the price information from theelectricity futures market can comprises price information for a fixedwindow of time from a day-ahead market, and the price information fromthe local resource allocation market can comprise price information fora rolling window of time. In certain implementations, the electricityfutures market operates using a longer time interval than the localresource allocation market. In some implementations, an indication of acurrent state of the electrical device and a requested quantity forelectricity are transmitted along with the bid value. In certainimplementations, an indication of a dispatched value for a current ornext upcoming time frame for the local resource allocation market isreceived from the computer that operates the local resource allocationmarket, the bid value is compared to the dispatched value, and a signalto activate the electrical device is generated based on the comparison.In some implementations, an expected dispatch value is computed from thedispatched value, one or more earlier dispatched values, and the priceinformation from the electricity futures market; and the desiredperformance value is adjusted based at least in part on the expecteddispatch value. In certain implementations, the electrical device is oneof an air-conditioning unit, heating unit, hot water heater,refrigerator, dish washer, washing machine, dryer, oven, microwave oven,pump, home lighting system, electric vehicle charger, or home electricalsystem.

Another embodiment disclosed herein is a method for generating a bidvalue for purchasing electricity in a market-based resource allocationsystem. In this embodiment, an indication of a current status of asystem controlled by an electrical device is received. A dispatchedindex values is received from a day-ahead market for electricity. A bidvalue for purchasing electricity is computed, the bid value being basedat least in part on the indication of the current status of the systemand based at least in part on the dispatched index values from theday-ahead market for electricity. The bid value is then transmitted to acomputer that operates a local resource allocation market for theelectricity. In certain implementations, a user comfort setting selectedby a user is received. The user comfort setting can be selected from atleast a first user comfort setting and a second user comfort setting,the first user comfort setting indicating the user's willingness to paymore to achieve a desired status of the system controlled by theelectrical device relative to the second user comfort setting, and thebid value can be additionally based at least in part on the user comfortsetting. In some implementations, the electrical device is a pump andthe current status is a measurement of a water level affected by thepump. In other implementations, the electrical device is an electriccharger for charging a battery, and the current status of the system isthe state of charge of the battery.

Another embodiment disclosed herein is a method for generating a bidvalue for purchasing electricity in a market-based resource allocationsystem. In this embodiment, a user comfort setting selected by a user isreceived, the user comfort setting being selected from at least a firstuser comfort setting and a second user comfort setting, the first usercomfort setting indicating the user's willingness to pay more to achievea desired performance level for an electrical device relative to thesecond user comfort setting. A cleared price for electricity is receivedfrom a local resource allocation market from which the electrical devicereceives electricity. Price information is received from an electricityfutures market. A probability value of operating the electrical deviceis computed based at least in part on the user comfort setting, thecleared price for electricity from the local resource allocation market,and the price information from the electricity futures market. A randomnumber is generated. A determination is made as to whether to operatethe electrical device by comparing the random number to the probabilityvalue. A signal for causing the electrical device to operate isgenerated based on the comparison. In some implementations, theelectricity futures market is a day-ahead electricity market. In certainimplementations, the price information from the electricity futuresmarket comprises cleared prices from the electricity futures market, andthe computing of the probability value is performed using an average ofthe cleared prices from the electricity futures market and a standarddeviation of the cleared prices from the electricity futures market. Theaverage and the standard deviation can be computed with local computinghardware or received from a remote source. In some implementations, thecomputing of the probability value is performed based at least in parton the price information from the electricity futures market and atleast in part on price information from the local resource allocationmarket. For example, the computing of the probability value can beperformed based at least in part on one or more weighted sums computedfrom the price information from the electricity futures market and fromthe price information for the local resource allocation market. Further,one or more of the weighted sums can be controlled by a variableweighting factor. In certain implementations, the price information fromthe electricity futures market comprises price information from a fixedwindow of time from a day-ahead electricity market, and the priceinformation from the local resource allocation market comprises priceinformation for a rolling window of time. In some implementations, theelectrical device is one of an air-conditioning unit, heating unit, hotwater heater, refrigerator, dish washer, washing machine, dryer, oven,microwave oven, pump, home lighting system, electric vehicle charger, orhome electrical system.

Another disclosed embodiment is a method for generating an offer valuefor offering to supply electricity in a market-based resource allocationsystem. In this embodiment, an offer value indicative of a value atwhich electricity can be supplied by a generator for a current or nextupcoming time frame is computed, the offer value being based at least inpart on dispatched index value information from an electricity futuresmarket. The offer value along with a value indicative of a quantity ofelectricity that can be supplied by the generator during the current orthe next upcoming time frame are submitted to the resource allocationsystem controlled by the resource allocation market. A dispatched indexvalue for the current or upcoming time frame is received from theresource allocation market. In some implementations, the dispatchedindex value is compared to the offer value, and the generator isactivated in response to the comparison. In certain implementations, theelectricity futures market is a day-ahead electricity market. In someimplementations, the dispatched index value information comprisescleared prices associated with the electricity futures market, and thecomputing of the offer value is performed using an average of thecleared prices from the electricity futures market and a standarddeviation of the cleared prices from the electricity futures market. Theaverage and the standard deviation can be computed locally or receivedfrom a remote source. In certain implementations, the computing of theoffer value is performed based at least in part on historical dispatchedindex values for electricity from the electricity futures market and atleast in part on historical dispatched index values from the localresource allocation market. For instance, the computing of the offervalue can be performed based at least in part on one or more weightedsums computed from the historical dispatched index values from theelectricity futures market and the historical dispatched index valuesfrom the local resource allocation market. Further, in certainimplementations, one or more of the weighted sums are controlled by avariable weighting factor.

Another disclosed embodiment is a method of operating a resourceallocation system. In this embodiment, a plurality of requests forelectricity are received from a plurality of end-use electrical devices,each of the requests indicating a requested quantity of electricity, adevice-requested index value indicative of a maximum price a respectiveend-use electrical device will pay for the requested quantity ofelectricity, and an indication of whether the respective end-useelectrical device is currently active. A responsive load currentlyexperienced in the resource allocation system is computed by summing therequested quantities of electricity from one or more of the electricaldevices that also are currently active. An unresponsive load in theresource allocation system is then computed by computing a differencebetween the responsive load and a total load currently experienced bythe resource allocation system. In certain implementations, a dispatchedindex value at which electricity is to be supplied is determined basedat least in part on the device-requested index values and theunresponsive load. In particular implementations, the dispatched indexvalue is determined using an auction process, and the unresponsive loadis submitted to the auction process as a request for electricity havinga requested quantity of electricity and a requested index value, therequested quantity of electricity corresponding to the unresponsiveload. The requested index value for the unresponsive load can be higherthan all other requests for electricity (e.g., at a market price cap).In some implementations, a plurality of offers for supplying electricityis received from a plurality of resource suppliers, each of the offersindicating an offered quantity of electricity and a supplier-requestedindex value indicative of a minimum price for which a respectivesupplier will produce the offered quantity of electricity. In suchimplementations, the dispatched index value can be determined based atleast in part on the device-requested index values, thesupplier-requested index values, and the unresponsive load. In certainimplementations, the acts of receiving the plurality of requests forelectricity, computing the responsive load, and computing theunresponsive load are repeated at periodic intervals. In someimplementations, the dispatched index value is transmitted to at leastone of the end-use electrical devices.

Embodiments of the disclosed methods can be performed using computinghardware, such as a computer processor or an integrated circuit. Forexample, embodiments of the disclosed methods can be performed bysoftware stored on one or more non-transitory computer-readable media(e.g., one or more optical media discs, volatile memory components (suchas DRAM or SRAM), or nonvolatile memory or storage components (such ashard drives)). Such software can be executed on a single computer or ona networked computer (e.g., via the Internet, a wide-area network, alocal-area network, a client-server network, or other such network).Embodiments of the disclosed methods can also be performed byspecialized computing hardware (e.g., one or more application specificintegrated circuits (“ASICs”) or programmable logic devices (such asfield programmable gate arrays (“FPGAs”)) configured to perform any ofthe disclosed methods). Additionally, any intermediate or final resultcreated or modified using any of the disclosed methods can be stored ona non-transitory storage medium (e.g., one or more optical media discs,volatile memory or storage components (such as DRAM or SRAM), ornonvolatile memory or storage components (such as hard drives)) and areconsidered to be within the scope of this disclosure. Furthermore, anyof the software embodiments (comprising, for example,computer-executable instructions which when executed by a computer causethe computer to perform any of the disclosed methods), intermediateresults, or final results created or modified by the disclosed methodscan be transmitted, received, or accessed through a suitablecommunication means.

The foregoing and other objects, features, and advantages of theinvention will become more apparent from the following detaileddescription, which proceeds with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a computing environment that canbe used to implement embodiments of the disclosed technology.

FIG. 2 is a schematic block diagram of a network topology that can beused to implement embodiments of the disclosed technology.

FIG. 3 is a block diagram of an exemplary resource allocation systemthat can be nested to any arbitrary depth with consumers making demandrequests and producers making supply offers.

FIG. 4 is a block diagram showing a resource consumer who makes demandrequests to a local resource allocation system and who consumes theresource based on the dispatched allocation index. The local resourceallocation system in FIG. 4 aggegrates the consumer's demand requestwith other requests.

FIG. 5 is a block diagram showing a resource producer who makes supplyoffers to a local resource allocation system and who supplies theresource based on the dispatched allocation index. The local resourceallocation system in FIG. 5 aggegrates the producer's supply offer withother offers.

FIG. 6 is a flowchart showing a generalized method for clearing offersand requests as can be used in any of the disclosed resource allocationsystems.

FIG. 7 is a flowchart showing a general embodiment for computing bids inany of the disclosed recourse allocation system using two-waycommunications.

FIG. 8 is a flowchart showing another general embodiment for computingbids in any of the disclosed recourse allocation system using two-waycommunications.

FIG. 9 is a flowchart showing a general embodiment for computing bids inany of the disclosed recourse allocation system using one-waycommunications.

FIG. 10 is a flowchart showing a general embodiment for generating offervalues for use in any of the disclosed recourse allocation systems.

FIG. 11 is a graph illustrating an example of capacity market buyer andsupply curves.

FIG. 12 is a diagram illustrating a bid and response strategy forthermostatically controlled loads according to one exemplary embodimentof the disclosed technology.

FIGS. 13-24 are diagrams illustrating exemplary market clearingscenarios.

DETAILED DESCRIPTION I. General Considerations

Disclosed below are representative embodiments of methods, apparatus,and systems for distributing a resource (such as electricity) using amarket-based resource allocation system. The disclosed methods,apparatus, and systems should not be construed as limiting in any way.Instead, the present disclosure is directed toward all novel andnonobvious features and aspects of the various disclosed embodiments,alone and in various combinations and subcombinations with one another.Furthermore, any features or aspects of the disclosed embodiments can beused in various combinations and subcombinations with one another. Thedisclosed methods, apparatus, and systems are not limited to anyspecific aspect or feature or combination thereof, nor do the disclosedembodiments require that any one or more specific advantages be presentor problems be solved.

Although the operations of some of the disclosed methods are describedin a particular, sequential order for convenient presentation, it shouldbe understood that this manner of description encompasses rearrangement,unless a particular ordering is required by specific language set forthbelow. For example, operations described sequentially may in some casesbe rearranged or performed concurrently. Moreover, for the sake ofsimplicity, the attached figures may not show the various ways in whichthe disclosed methods can be used in conjunction with other methods.Additionally, the description sometimes uses terms like “determine” and“generate” to describe the disclosed methods. These terms are high-levelabstractions of the actual operations that are performed. The actualoperations that correspond to these terms may vary depending on theparticular implementation and are readily discernible by one of ordinaryskill in the art. Furthermore, as used herein, the term “and/or” meansany one item or combination of items in the phrase.

Any of the disclosed methods can be implemented usingcomputer-executable instructions stored on one or more computer-readablemedia (e.g., non-transitory computer-readable media, such as one or moreoptical media discs, volatile memory components (such as DRAM or SRAM),or nonvolatile memory components (such as hard drives)) and executed ona computer (e.g., any commercially available computer). Any of thecomputer-executable instructions for implementing the disclosedtechniques (e.g., the disclosed bid generation, offer generation, ordispatch index generation techniques) as well as any intermediate orfinal data created and used during implementation of the disclosedresource allocation systems can be stored on one or morecomputer-readable media (e.g., non-transitory computer-readable media).The computer-executable instructions can be part of, for example, adedicated software application or a software application that isaccessed or downloaded via a network (e.g., through a web browser). Morespecifically, such software can be executed on a single computer (e.g.,any suitable commercially available computer) or in a networkenvironment (e.g., via the Internet, a wide-area network, a local-areanetwork, a client-server network, or other such network).

For clarity, only certain selected aspects of the software-basedembodiments are described. Other details that are well known in the artare omitted. For example, it should be understood that thesoftware-based embodiments are not limited to any specific computerlanguage or program. For instance, embodiments of the disclosedtechnology can be implemented by software written in C++, Java, Perl,JavaScript, Adobe Flash, or any other suitable programming language.Likewise, embodiments of the disclosed technology are not limited to anyparticular computer or type of hardware. Details of suitable computersand hardware are well known and need not be set forth in detail in thisdisclosure.

Furthermore, any of the software-based embodiments (comprising, forexample, computer-executable instructions which when executed by acomputer cause the computer to perform any of the disclosed methods) canbe uploaded, downloaded, or remotely accessed through a suitablecommunication means. Such suitable communication means include, forexample, the Internet, the World Wide Web, an intranet, softwareapplications, cable (including fiber optic cable), magneticcommunications, electromagnetic communications (including RF, microwave,and infrared communications), electronic communications, or other suchcommunication means.

The disclosed methods can also be implemented by specialized computinghardware that is configured to perform any of the disclosed methods. Forexample, the disclosed methods can be implemented by an integratedcircuit (e.g., an application specific integrated circuit (“ASIC”) orprogrammable logic device (“PLD”), such as a field programmable gatearray (“FPGA”)). The integrated circuit or specialized computinghardware can be embedded in or directly coupled to an electrical device(or element) that is configured to interact with the resource allocationsystem. For example, the integrated circuit can be embedded in orotherwise coupled to a generator (e.g., a wind-based generator,solar-based generator, coal-based generator, or nuclear generator); anair-conditioning unit; heating unit; heating, ventilation, and airconditioning (“HVAC”) system; hot water heater; refrigerator; dishwasher; washing machine; dryer; oven; microwave oven; pump; homelighting system; electrical charger; electric vehicle charger; homeelectrical system; or any other electrical system having variableperformance states.

FIG. 1 illustrates a generalized example of a suitable computinghardware environment 100 in which several of the described embodimentscan be implemented. The computing environment 100 is not intended tosuggest any limitation as to the scope of use or functionality of thedisclosed technology, as the techniques and tools described herein canbe implemented in diverse general-purpose or special-purposeenvironments that have computing hardware.

With reference to FIG. 1, the computing environment 100 includes atleast one processing unit 110 and memory 120. In FIG. 1, this most basicconfiguration 130 is included within a dashed line. The processing unit110 executes computer-executable instructions. In a multi-processingsystem, multiple processing units execute computer-executableinstructions to increase processing power. The memory 120 may bevolatile memory (e.g., registers, cache, RAM), non-volatile memory(e.g., ROM, EEPROM, flash memory), or some combination of the two. Thememory 120 stores software 180 for implementing one or more of thedescribed techniques for operating or using the disclosed resourceallocation systems. For example, the memory 120 can store software 180for implementing any of the disclosed dispatch index determination,bidding, or offer strategies described herein and their accompanyinguser interfaces.

The computing environment can have additional features. For example, thecomputing environment 100 includes storage 140, one or more inputdevices 150, one or more output devices 160, and one or morecommunication connections 170. An interconnection mechanism (not shown)such as a bus, controller, or network interconnects the components ofthe computing environment 100. Typically, operating system software (notshown) provides an operating environment for other software executing inthe computing environment 100, and coordinates activities of thecomponents of the computing environment 100.

The storage 140 can be removable or non-removable, and includes magneticdisks, magnetic tapes or cassettes, CD-ROMs, DVDs, or any other tangiblenon-transitory storage medium which can be used to store information andwhich can be accessed within the computing environment 100. The storage140 can also store instructions for the software 180 implementing any ofthe described techniques, systems, or environments.

The input device(s) 150 can be a touch input device such as a keyboard,mouse, touch screen, pen, or trackball, a voice input device, a scanningdevice, or another device that provides input to the computingenvironment 100. The output device(s) 160 can be a display, printer,speaker, CD-writer, or another device that provides output from thecomputing environment 100.

The communication connection(s) 170 enable communication over acommunication medium to another computing entity. The communicationmedium conveys information such as computer-executable instructions,resource allocation messages or data, or other data in a modulated datasignal. A modulated data signal is a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia include wired or wireless techniques implemented with anelectrical, optical, RF, infrared, acoustic, or other carrier.

The various methods, systems, and interfaces disclosed herein can bedescribed in the general context of computer-executable instructionsstored on one or more computer-readable media. Computer-readable mediaare any available media that can be accessed within or by a computingenvironment. By way of example, and not limitation, with the computingenvironment 100, computer-readable media include tangible non-transitorycomputer-readable media, such as memory 120 and storage 140.

The various methods, systems, and interfaces disclosed herein can alsobe described in the general context of computer-executable instructions,such as those included in program modules, being executed in a computingenvironment on a target processor. Generally, program modules includeroutines, programs, libraries, objects, classes, components, datastructures, and the like that perform particular tasks or implementparticular abstract data types. The functionality of the program modulesmay be combined or split between program modules as desired in variousembodiments. Computer-executable instructions for program modules may beexecuted within a local or distributed computing environment.

II. Exemplary Network Topology for Implementing a Resource AllocationMarket

An example of a network topology for implementing a resource allocationmarket 200 according to the disclosed technology is depicted in FIG. 2.Networked computing devices 220, 222, 230, 232, 250 can be, for example,computing devices having computing hardware that runs software foraccessing one or more central computers 210 or that are otherwiseconfigured to communicate with the one or more central computers 210.The one or more central computers 210 in the illustrated embodimentmanage and operate the resource allocation market 200. For example, theone or more central computers 210 can be associated with an operator whomanages an electrical transmission network that serves or is served byelectrical loads or resources associated with the computing devices 220,222, 230, 232, 250. For instance, the one or more central computers 210can be associated with a distribution substation, a sub-transmissionsubstation, a transmission substation, or other such transmission nodein a power transmission network.

The computing devices 220, 222, 230, 232, 250 and the central computer210 can have computer architectures as shown in FIG. 1 and discussedabove. The computing devices 220, 222, 230, 232, 250 are not limited totraditional personal computers or servers but can comprise othercomputing hardware configured to connect to and communicate with anetwork 212 (e.g., specialized computing hardware associated with anelectrical device or a power generator (e.g., hardware comprising anintegrated circuit (such as an ASIC or programmable logic device)configured to perform any of the disclosed methods)).

In the illustrated embodiment, the computing devices 220, 222, 230, 232,250 are configured to connect to one or more central computers 210(e.g., via network 212). In certain implementations, the centralcomputer receives resource bids or requests from those computing devicesassociated with resource consumers (e.g., devices 220, 222) and receivesresource offers from those computing devices associated with resourcesuppliers (e.g., devices 230, 232, 250). The one or more centralcomputers 210 then compute a value at which the resource is to bedispatched (e.g., using a double auction technique) and transmit thisdispatched value to the computing devices 220, 222, 230, 232, 250. Whenthis dispatched value refers to the actual price of the resource, it issometimes referred to as the “real-time price” and indicates theclearing price of the current time interval or of the next upcoming timeinterval (e.g., the imminent time interval). In some implementations,this price is also known as the real-time locational marginal price. Thetime intervals can vary, but in certain implementations is less than onehour (such as 30 minutes or less, 15 minutes or less, 10 minutes orless, 5 minutes or less, or any other interval).

The one or more central computers 210 can also transmit additional datato one or more of the computing devices 220, 222, 230, 232, 250. Theadditional data can be used by the computing devices 220, 222, 230, 232,250 to compute a demand bid or supply bid. For example, the additionaldata can include price information from a forward-looking or futuresmarket (e.g., price information from a day-ahead market). For example,the price can be price information for the same interval as the currentinterval or the next upcoming interval but in the following day (e.g., aprice from the day-ahead market, such as a day-ahead locational marginalprice). The price information from the futures market may not beavailable in the same duration of time interval as the real-time price(e.g., the day-ahead price may apply to a longer interval (such as 1hour) when compared to the interval of the real-time price information)but can overlap with the current interval or the next upcoming interval.For purposes of this disclosure, the price information from aforward-looking or futures market is generally referred to herein as a“future price.”

The price information from a futures market is typically available frompower transmission entities or other power industry entities thatmaintain or participate in a forward-looking market, and most commonlyrefers to price information from the day-ahead market but can be priceinformation from a market for other future time periods (e.g., for afuture time period other than the next upcoming time interval in therelevant resource allocation system for which a dispatch value is beingcomputed). Briefly, the day-ahead market refers to a financial marketwhere market participants purchase and sell energy at financiallybinding day-ahead prices for the following day. As a result of theday-ahead market, a financially binding schedule of commitments for thepurchase and sale of energy is developed each day based on the bid andoffer data submitted by the market participants. The day-ahead marketallows buyers and sellers to lock in their price and hedge againstvolatility in the real-time energy market. Examples of such day-aheadmarkets include the day-ahead market operated by PJM InterconnectionLLC.

In the embodiment illustrated in FIG. 2, the price information from afutures market (e.g., the day-ahead market information) is accessed andreceived by the one or more central computers 210 from a provider 250(e.g., a regional transmission provider or transmission provider at thenext highest hierarchical level in the transmission network).Furthermore, as shown in the illustrated embodiment, the provider 250 isitself a resource supplier that bids an offer to the market resourceallocation market 200. For example, the provider 250 can be a regionaltransmission provider that offers electricity at a wholesale price tothe resource allocation market 200 operated by the one or more centralcomputers 210.

In still further embodiments, the additional information may includeinformation computed from the real-time price information and/or fromthe price information from the futures market. For example, inparticular implementations, the additional information is used tocompute other derivative values at the one or more central computers 210that are then transmitted to the computing devices 220, 222, 230, 232,250. For example, and as more fully explained below, an average priceand/or a standard deviation can be computed from the real-time price orfrom the price information from the futures market. The average pricecan be computed from prices of adjacent intervals (e.g., prices in thepreceding m intervals) or from prices at the same interval from earlierdays (e.g., prices from the same interval (such as the one hour intervalbeginning at 9:00 p.m.) from the preceding n days). The average priceand/or standard deviation can then be transmitted to the computingdevices 220, 222, 230, 232, 250. In still other embodiments, a singleprice deviation value can be computed by dividing the average of thereal-time prices or the future prices over some interval and dividing bythe standard deviation (e.g., the value can be the average/standarddeviation) or by dividing the difference between the cleared real-timeor futures price and the average of the real-time or future prices oversome interval by the standard deviation (e.g.,P_(dev)=(P_(clear)−P_(average))/σ_(actual)). This price deviation,referred to herein as v, is the number of standard deviations thereal-time or futures price is from the average price and, in someembodiments, can be the only signal used to control the system. In stillfurther embodiments, one or more of the computing devices 220, 222, 230,232, 250 themselves compute the other derivative values from thereal-time price information, the future price information, or both thereal-time price information and the future price information.

Although the information shown as being exchanged in FIG. 2 isidentified as price information, the information may be generalized toinformation that is indicative of a price or relates to an index that iscapable of being monetized. For example, the real-time price can be areal-time dispatched index value, and the price information from afutures market can be dispatched index values from a futures market.

In the illustrated embodiment, the one or more central computers 210 areaccessed over a network 212, which can be implemented as a Local AreaNetwork (“LAN”) using wired networking (e.g., the Ethernet IEEE standard802.3 or other appropriate standard) or wireless networking (e.g. one ofthe IEEE standards 802.11a, 802.11b, 802.11g, or 802.11n or otherappropriate standard). Furthermore, at least part of the network 212 canbe the Internet or a similar public network. The one or more centralcomputers 210 can be located at a transmission node for the resourceallocation market itself (e.g., at a distribution substation,sub-transmission substation, transmission substation, or othertransmission node locale) or can alternatively be located remotely(e.g., at a centralized location that is responsible for managing andoperating multiple resource allocation markets).

The various possible roles and functionalities of the computing devices220, 222, 230, 232, 250 and the one or more central computers 210 willbe described in more detail in the following sections.

III. Embodiments of the Disclosed Resource Allocation Scheme

A. General Case of a Nested, Hierarchical Resource Allocation Schema

FIG. 3 is a schematic block diagram illustrating an embodiment of aresource allocation system 300 according to the disclosed technology. Inthe embodiment shown in FIG. 3, system 300 comprises multiple nestedresource allocation systems (two of which are shown as subsystems 310and 312 and which can operate using the network topology illustrated inFIG. 2), which themselves comprise self-similar resource allocationsubsystems. The resource allocation system 300 can be nested to anyarbitrary depth with net producers (such as net producer 320) makingsupply offers and net consumers (such as net consumer 322) making demandrequests to a larger bulk system 340. In certain embodiments, the system300 does not include nested subsystems, but comprises a single-levelsystem in which a central system (e.g., a distribution service provider)communicates with consumers and suppliers that have no nestedsubsystems. Any of the nested subsystems can operate using a networktopology such as that shown in FIG. 2 for operating a local resourceallocation market.

All resources that are limited in some manner and can be measured can beallocated independently in such a system. The embodiments disclosedherein generally concern applying the resource allocation system 300 toan electrical power grid in which electrical power is limited, but it isto be understood that this application is not limiting. The resourceallocation system can be used in other contexts as well, including watersupply, Internet wireless bandwidth distribution, or other such marketshaving limited resources.

In the illustrated embodiment, each of the resource allocation marketsoperates by periodically collecting demand requests from consumers andsupply offers from resource suppliers and determining an index value(e.g., a “price” or “cleared price”) at which the resource allocation isto be dispatched. As more fully explained below, the dispatched indexvalue is determined from the demand requests and supply offers. In oneparticular embodiment, the process is different than traditional marketsin that an index that is capable of being monetized (rather than just acurrency value itself) is used. The index provides a common valuationmethod for participants in the system. For ease of presentation,reference will sometimes be made in this disclosure to the index for aresource as though it were the actual price of the resource. It is to beunderstood that such reference includes not only the situation where theindex is the currency, but also the situation where the index is anotherindex unit that is capable of being monetized or traded.

B. Participants and Accounts

In one embodiment of the disclosed technology, at least some of theparticipants in the system have accounts in which the fund of indexunits at their disposal is kept. As consumers use resources, their indexfund balances are debited, and as producers deliver resources, theirindex fund balances are credited. Index funds can be credited using avariety of mechanisms, including up-front deposits (e.g., throughincentives), periodic deposits (e.g., with income), or purchased fundsfrom a separate index fund market when producers sell funds.

C. Supply Offers and Demand Requests

In one exemplary embodiment of the disclosed technology, end-useconsumers use computing devices (e.g., transactive controllers or activecontrollers) to request resources from their local distribution serviceprovider based on their current needs (e.g., the needs of the appliancesor electrical devices in the consumer's residence or business). Forexample, end-use consumers can input their resource requests through aweb site that transmits the user's requests over the Internet to one ormore central computers that are used by the distribution serviceprovider to allocate the resource. In such instances, the requests canbe computed and transmitted by executing computer-executableinstructions stored in non-transitory computer-readable media (e.g.,memory or storage). Alternatively, a consumer's end-use appliances orelectrical devices can be configured to themselves compute the resourcerequests (in which case the appliance or device can be considered as theend-use consumer or resource consumer). In such instances, the requestscan be computed using computing equipment (e.g., a transactivecontroller or active controller) embedded in the appliances orelectrical devices themselves. In still another embodiment, computingequipment at the consumer's residence or locale can collect informationfrom one or more of the consumer's appliances or electrical devices andtransmit aggregated requires to the local distribution service provider.

The computing equipment can comprise a computer system (e.g., aprocessor and non-transitory computer-readable media storingcomputer-executable instructions) or can comprise a specializedintegrated circuit (e.g., an ASIC or programmable logic device)configured to compute the resource request or offer. If the requests arecomputed by the appliances or electrical devices themselves, therequests can be directly sent to the one or more central computers ofthe distribution service provider (e.g., via the Internet) or can beaggregated with other requests (e.g., using a computer or othercomputing equipment at the consumer's home). For instance, theappliances and electrical devices at the consumer's home can transmittheir requests (e.g., wirelessly using Wi-Fi or the like) to a localcomputer, or a computer-based home energy management system (“HEMS”),which aggregates the requests. The aggregated requests can then be senttogether to the distribution service provider (e.g., as a single requestto the central computer or as a single message comprises a string ofrequests).

In one exemplary embodiment, resource requests comprise at least twopieces of information: the quantity of the resources desired (described,for example, as a rate of consumption for the time frame over which theresource will be allocated) and the requested index value. In someembodiments, the requested index value is the maximum index value atwhich the quantity will be consumed. Desirably, resource consumerssubmit at least one such request for each time frame in which they wishto consume, and the time frame is determined by the local distributionservice provider. The time frame can vary from embodiment to embodiment,but in some embodiments is 60 minutes or less, 15 minutes or less, or 5minutes or less, and some embodiments can use mixed time and/oroverlapping frames. As more fully explained below, the time frame candepend on the size of the resource allocation system and the number ofnested resource allocation markets within the overall system. Ingeneral, the time frame used in a lower-level system in a nestedframework will be less than the time frame for a higher-level system inthe nested framework. After receiving such requests within the timeframe, the local distribution service provider can compute and dispatchthe index value at which each resource is allocated. This value issometimes referred to herein as the “dispatched index value” or“dispatched value.” In applications where the value is the actual price,this value is sometimes referred to herein as the “settled price,”“clearing price,” or “real-time price”

In one exemplary embodiment, resource suppliers use computing devices(e.g., transactive controllers) to submit offers for resources to thelocal distribution service provider based on the current cost ofproviding the resources. Resource suppliers can include, for example,utility substations at the same or higher transmission level (e.g.,transmission substations, subtransmission substations, or distributionsubstations), merchant generators (e.g., large-scale power generatorsusing coal-, nuclear-, wind-, solar-, hyro-, or geothermal-based powergeneration), local generators (e.g., diesel generators or smaller scalesolar or wind generators) or consumer-based generators (e.g., electricvehicles). For example, the supply offers can be computed and submittedover the Internet using a computer system (e.g., using a dedicated website). Alternatively, the supply offer can be computed and transmittedusing a specialized integrated circuit configured to compute theresource offer (e.g., an ASIC or programmable logic device). Any suchcomputing hardware can be coupled directly to and provide control overthe relevant equipment for supplying the resource. For instance, thecomputing hardware can be integrated with the control equipment for anelectrical power generator, thereby allowing the computing hardware todirectly activate and deactivate the generator as needed.

In one exemplary embodiment, offers comprise at least two pieces ofinformation: the quantity of the resources available (described, forexample, as a rate of production for the time frame over which theresource will allocated) and the requested index value for the quantityof the resource. In some embodiments, the requested index value is theminimum index value at which the resource will be produced. Producersdesirably submit at least one such offer for each time frame in whichthey wish to produce resources, and the time frame is determined by theservice provider.

In one exemplary embodiment for operating the resource allocationsystem, consumers are required to consume the resources which theyrequested only if they requested the resource at an index value greaterthan or equal to the dispatched index value. Conversely, consumers areprohibited from consuming the resources if they requested the resourceat an index value less than the dispatched index value for that timeframe. These rules can be enforced, for example, at the appliance orelectrical device level (e.g., using appropriate shut-off hardware) orenforced by control signals sent from a computer at the consumer's homeor locale to the relevant appliance or electrical equipment. Violationof these rules can be subject to a penalty (e.g., a penalty leviedagainst the offender's index fund account). Furthermore, in someembodiments of the disclosed technology, consumers can submitunconditional requests that require the distribution service provider todeliver the resource at any price, and require the consumer to accept itat any price.

Similarly, in one exemplary embodiment for operating the resourceallocation system, producers are required to produce the resources whichthey offered only if they offered the resource at an index value lessthan or equal to the dispatched index value. Conversely, producers areprohibited from producing resource if they offered the resource at anindex value greater than the dispatched index value for that time frame.Violation of the rules can be subject to a penalty levied against theirindex fund accounts. Furthermore, in some embodiments of the disclosedtechnology, producers can submit unconditional offers that require thedistribution service provider to accept the resource at any price, andrequire the producer to supply it at any price.

D. Aggregation Services

In certain embodiments of the disclosed technology, and as noted above,a service provider may in turn be a consumer or producer with respect toanother service provider, depending on whether they are a net importeror exporter of resources. Examples of such arrangements are shown inblock diagrams 400 and 500 of FIGS. 4 and 5, respectively. Inparticular, FIG. 4 is a diagram 400 showing a local resource consumer410 that makes demands on a local distribution service provider 412(e.g., a local feeder), who in turn aggregates local requests to make anaggregated bulk request to a service provider at the next-higher levelin the hierarchy. In the illustrated embodiment, the service provider atthe next-higher level is a bulk distribution service provider 414 (e.g.,a regional distribution service provider), but can be anotherhigher-order service provider in the system (e.g., a subtransmissionsubstation, transmission substation, or other such provider).

FIG. 5 is a diagram 500 showing a local producer 510 that makes offersto a local distribution provider 512 (e.g., a local feeder), who in turnaggregates local offers to make an aggregated offer to a serviceprovider at a next-higher level in the hierarchy. In the illustratedembodiment, the service provider at the next-higher level is a bulkdistribution provider 514 (e.g., a regional distribution serviceprovider), but can be another higher-order service provider in thesystem (e.g., a subtransmission substation, transmission substation, orother such provider).

Any number of service providers can be combined to construct a system ofarbitrary size and complexity.

In certain embodiments of the disclosed technology, producers andconsumers can make non-firm offers and requests as well, but suchrequests can have an index premium with respect to the firm offers andrequests presented during a given time frame. The premium can be based,for example, on the difference between the aggregate cost of loadfollowing in the service providers system and the cost the same in thebulk system (load following service cost arbitrage).

E. Multiple Time Frames

As resources are aggregated to larger and larger system, the time frameover which allocation is performed can be lengthened. For example, thelowest-level localized resources (e.g., feeder-level resources) might bedispatched on a 5 minute time frame, mid-level resources (e.g.,regional-level or transmission-level resources) might be dispatched on a15 minute time frame, and highest-level resources (e.g., bulk-grid-levelresources) transmission resources might be dispatched on a 1 hour timeframe. This permits aggregators to also aggregate over time byexchanging or moving blocks of resources across time frames usingstorage capacities and ramp rates.

Both consumers and producers can break their total demand and supplyinto multiple requests and offers spanning multiple time frames. Forexample, in the face of 10% uncertainty (or other percentage ofuncertainty) in the quantity needed, a consumer can request the meanquantity of the needed resources in a longer time frame at any price andexchange (buy or sell) the remaining 10% fluctuation (or otherpercentage of fluctuation) in a shorter time frame at any price.

Additionally, in certain embodiments, bids can be submitted to thehigher-level markets for resources that are to be supplied during one ormore future time frames that are not imminent (e.g., time frames thatoccur during the following day, following two days, following week, orany such future time frame). Such bids for future time windows can be inaddition to the bid for the next time interval and can be used to helpsecure power and settle the bulk-resource market in advance of theactual power needs. In some implementations, such bids for future timewindows indicate a future energy need (kWh) rather than an imminentcurrent power need (kW).

F. An Exemplary Resource Allocation Strategy

In each time frame, and at one or more of the hierarchical levels of theresource allocation system, the dispatched index value and quantityallocated is determined by a resource allocation service. The resourceallocation service can be implemented by one or more computing devicesat each level of the system and according to a network topology such asthat shown in FIG. 2. Furthermore, the resource allocation service canbe implemented using computer-executable instructions stored on one ormore computer-readable media (e.g., non-transitory computer-readablemedia, such as storage or memory) which when executed by a computer willcause the computer to perform a resource allocation method, such as anyof the techniques described below.

A wide variety of methods can be used to determine the dispatched indexvalue. In certain embodiments, however, the dispatched index value isdetermined using a double auction technique. For instance, in oneparticular embodiment, the following technique is used. The requests andoffers are separated into two groups. Each group is sorted by the indexvalue provided, requests being sorted by descending value, and offers byascending value (or vice versa). Next, each item in the sorted lists isgiven a quantity level computed by adding its quantity to the previousitem's quantity level, with the first items quantity level being itsquantity alone. Finally, the dispatched index value is found bydetermining the index value at which the same quantity level forrequests and offers occurs. In one embodiment, this can occur in one oftwo ways. Either two requests bind a single offer, in which case thesupplier is required to supply less than the offered quantity and theoffer index value is the dispatched index value; or two offers bind asingle request, in which case the consumer is required to consume lessthan the requested quantity with only partial resources and the requestindex value is the dispatched index value. Additionally, there are somespecial cases that although rare are desirably handled explicitly.Whenever both consumers and suppliers mutually bind each other at agiven quantity level, the dispatched index value can be the mean of theoffer and request indexes, the request index, or the offer index. Incertain embodiments, the method that maximizes the total benefit (e.g.,profit) to both consumers and producers is chosen and in cases wheremore than one index level maximizes the total benefit, the index levelwhich most equitably divides the total benefit between consumers andproducers is chosen. Exemplary market clearing scenarios are discussedin greater detail below in Section III.M.

FIG. 6 is a flowchart 600 showing a generalized method for clearingoffers and requests as can be used in any of the disclosed resourceallocation systems. The particular method shown in FIG. 6 is for asystem for allocating electricity resources, but this usage should notbe construed as limiting. The method can be performed using computinghardware (e.g., a computer processor or a specialized integratedcircuit). For instance, the method can be performed by computinghardware such as shown in FIG. 1 that manages the resource allocationmarket for a particular subsystem or in the resource allocation scheme.

At 610, a plurality of requests for electricity are received from aplurality of resource consumers (e.g., electrical devices, homeconsumers, or other electrical service providers in the hierarchy). Therequests can comprise data messages indicating a requested quantity ofelectricity and a consumer-requested index value (e.g., a maximum pricea respective electrical-power user will pay for the requested quantityof electricity.

At 612, a plurality of offers for supplying electricity are receivedfrom a plurality of resource suppliers (e.g., local generators, merchantgenerators, or other electrical service providers in the hierarchy, suchas service providers at the next-highest hierarchical level). The offerscan comprise data messages indicating an offered quantity of electricityand a supplier-requested index value (e.g., a minimum price for which arespective supplier will produce the offered quantity of electricity).

At 614, a dispatched index value is computed at which electricity is tobe supplied based at least in part on the consumer-requested indexvalues and the supplier-requested index values. In some implementations,the act of determining the dispatched index value is performed using adouble auction method. For example, the act of determining thedispatched index value can comprise separating the requests and theoffers into two groups, sorting each item in the two groups according toa quantity level, and determining the dispatched index value bydetermining the index value at which the same quantity level forrequests and offers occurs.

At 616, the dispatched index value is transmitted to at least one of theconsumers or resource suppliers (e.g., using suitable communicationmeans, such as the Internet or other network). In certain embodiments,additional information is transmitted with the dispatch index value. Asnoted above with respect to FIG. 2, the additional information caninclude price information from a futures market (e.g, day-ahead priceinformation). Furthermore, in certain embodiments, other values can alsobe transmitted, such as values derived from the dispatched index value(e.g., an average and standard deviation of the dispatched index valueover a selected time frame) or values derived from the price informationfrom the futures market (e.g., an average and standard deviation of theday-ahead price).

Methods acts 610, 612, 614, and 616 can be repeated at periodicintervals (e.g., intervals of 60 minutes or less, 10 minutes or less, 5minutes or less, or other such interval). Furthermore, it should beunderstood that the method acts 610 and 612 do not necessarily occur inthe illustrated sequence. Instead, the orders and requests can bereceived substantially simultaneously. For instance, the orders andrequests can be received at various times and/or orders within a giventime period and before the dispatched index is determined.

G. Demand Strategies

In some cases, suppliers or consumers desirably place offers or bidsthat nearly guarantee that they obtain consumers or suppliers,respectively. To help generate an offer or request that has a highlikelihood of being accepted by the local resource allocation system, asupplier or consumer can use a recent history of dispatched index valuesfrom the local market and/or a recent history of the dispatched indexvalues from a futures market (e.g., a day-ahead market) to forecast themost likely dispatched index value for a particular offer or requesttime frame and to adjust the offer or request based on this information.This ability to adjust a request or offer allows a consumer or supplierto utilize an adaptive bidding or offer strategy. As more fullyillustrated below, such adaptive strategies are useful in a variety ofsettings.

One possible adaptive request strategy is to compute the average and thestandard deviation of the dispatched index values from the local marketand/or the local dispatched index values from the futures market (e.g.,the prices from the day-ahead market) over the last N time frames, whereN is a relatively large number compared to the time frame (e.g., 20, 50,100 or more). When consumers cycle their demand for resourcesperiodically, they can adjust the consumption time to exploit times whenthe index is low. In one particular embodiment, the control decision forconsumption can be offset by the computed average (or a weightedfunction of two or more computed averages (such as averages of thedispatched index values from the local market and dispatched indexvalues from the futures market)) and scaled by the computed standarddeviation (or a weighted function of two or more computed standarddeviations (such as standard deviations of the dispatched index valuesfrom the local market and dispatched index values from the futuresmarket)) before being submitted to the resource allocation system.

In some embodiments, the last N time frames that are used areconsecutive time frames. For instance, if N is selected to account forthe previous 24 hours, if the duration of a time frame is 5 minutes, andif the current time frame is 3:00 p.m., then the dispatched index valuefrom the 3:00 p.m. time frame the previous day, the index from the 3:05p.m. time frame the previous day, the index from the 3:10 p.m. timeframe the previous day, and so on, can be used. In other embodiments,the last N time frames that are used are from the same time frame (orsimilar time frame) as the current time frame but are from differentdays (e.g., consecutive prior days). For instance, if N is selected toaccount for the previous 7 days, if the duration of a time frame is 5minutes, and if the current time frame is 3:00 p.m., then the dispatchedindex value from the 3:00 p.m. time frame from the previous 7 days canbe used. Various combinations of these time frames can also be used(e.g., the index values for multiple time frames around the current timeframe from multiple previous days). This flexibility can help furtheraccount for variations in demand that arise throughout a day.

Many consumers that employ adaptive control also use a similar strategyto determine the operating point from the dispatched index value. Thiscan be done by adjusting the control set-point based on the dispatchedindex value.

The following paragraphs introduce general embodiments for generatingbid values in a resource allocation system, such as any of the resourceallocation systems disclosed herein. Specific implementations of thesegeneralized embodiments are introduced in Section IV below.

FIG. 7 is a flowchart 700 showing a generalized embodiment for computingbids in any of the disclosed recourse allocation system using two-waycommunications (e.g., a transactive controller). The particular methodshown in FIG. 7 is for an electrical device in a system for allocatingelectrical resources, but this usage should not be construed aslimiting. The electrical device can be a variety of devices, such as anair-conditioning unit; heating unit; heating, ventilation, and airconditioning (HVAC) system; hot water heater; refrigerator; dish washer;washing machine; dryer; oven; microwave oven; pump; home lightingsystem; electrical charger; electric vehicle charger; home electricalsystem; or any other electrical system having variable performancestates. The method of FIG. 7 can be performed using computing hardware(e.g., a computer processor or an integrated circuit). For instance, themethod can be performed by a computer at an end-user's locale or home, acomputer coupled to an electrical device, or by specialized hardware(e.g., an ASIC or programmable logic device) coupled to the electricaldevice. Furthermore, it should be understood that the method acts inFIG. 7 do not necessarily occur in the illustrated sequence.

At 710, a desired performance value indicative of a user's desiredperformance level for an electrical device is received (e.g., loaded,buffered into memory, or otherwise input and prepared for furtherprocessing). For example, a desired temperature for atemperature-controlled environment can be received.

At 712, one or more user tolerance values indicative of the user'swillingness to tolerate variations from the desired performance levelare also received (e.g., loaded, buffered into memory, or otherwiseinput and prepared for further processing). For example, a comfortsetting reflective of comfort versus economy (such as any of the comfortsettings shown in Table 1 below or similar comfort setting) can bereceived. In certain embodiments, the user tolerance value is selectedfrom at least a first tolerance value and a second tolerance value, thefirst tolerance value resulting in higher bid values relative to thesecond tolerance value. The comfort setting can comprise a single valuethat is representative of the user's tolerance, or can comprise multiplevalues that represent upper and/or lower limits of the performance ofthe electrical device (e.g., a low temperature and a high temperature).The performance value and user tolerance value can be input by the user,for example, through an appropriate graphical user interface displayedon a computer or through a keypad, touch screen, dial, or other controlmechanism associated with the electrical device.

At 714, a bid value for purchasing electricity sufficient to operate theelectrical device at the desired performance level is computed. In theillustrated embodiment, the bid value is based at least in part on thedesired performance value and the user tolerance value. In theillustrated embodiment, the bid value is additionally based at least inpart on one or more values indicative of the dispatched index valuesfrom a futures market (e.g., prices from a day-ahead market). Thedispatched index values from the futures market can be, for example, theday-ahead prices for the current day (e.g., a blocked 24-hour window).Further, the bid value can be additionally based at least in part on oneor more values indicative of dispatched index values for the localmarket (the market to which the bid value will be submitted). Thedispatched index values from the local market can be, for example, thedispatched index values from a previous time period (e.g., the previous3 hours) and can be updated on a rolling window basis. In certainembodiments, the bid value is based on a combination of the dispatchedindex values from the futures market and the dispatched index valuesfrom the local market (e.g., a weighted combination). Any of thesevalues can be computed locally at the electrical device, or can betransmitted from the one or more central computers.

Additionally, and as more fully explained below, the bid value can bebased on an average and/or a standard deviation of the dispatched indexvalues from the futures market. Likewise, the bid value can be based onan average and/or a standard deviation of the dispatched index valuesfrom the local market, or some combination of the averages and/orstandard deviations. In alternative embodiments, a single dispatchedindex value from a futures market and/or a single dispatched value(e.g., the most recently dispatched value) is used. In still otherembodiments, a value other than the average or standard deviation isderived from the multiple available values and used to perform themethod (e.g., a median value, weighted sum, or other such derivedvalue). Any of these values can be computed locally at the electricaldevice, or can be transmitted from the one or more central computers. Infurther embodiments, a current performance level of the electricaldevice can also be received. In such embodiments, the bid value can alsobe based at least in part on the current performance level.

At 716, the bid value is transmitted to one or more central computers(e.g., one or more computers that manage and operate a local resourceallocation market by, for instance, conducting the auction process forthe market) in the market-based resource allocation system (e.g., usingsuitable communication means, such as the Internet or other network). Incertain embodiments, the quantity of the resources desired (described,for example, as a rate of consumption for the time frame over which theresource will be allocated) is also transmitted to the one or morecentral computers. Additionally, in some embodiments, the current stateof the electrical device associated with the bid is also transmitted tothe one or more central computers (e.g., as a value that indicateswhether the electrical device is current on (or active) or off (orinactive)). As more fully explained below, this state information can beused by the operator of the resource allocation market to determine thenonresponsive load that is serviced by the local resource allocationmarket.

At 718, an indication of a dispatched index value for a current (or nextupcoming) time frame is received from the one or more central computers.In certain embodiments, a value other than the dispatched value isreceived from the one or more central computers but which is indicativeof the dispatched value. For example, the value can indicate adifference between a last dispatched value and a newly dispatched value.As noted, the one or more central computers can transmit otherinformation as well (e.g., one or more of dispatched index value(s) froma futures market, averages of the dispatched index values from thefutures market and/or the local market, or standard deviations of thedispatched index values from the futures market and/or the localmarket).

At 720, the bid value is compared to the dispatched value for thecurrent (or next upcoming) time frame, and a signal is generated toactivate or deactivate the electrical device based on this comparison(e.g., if the bid value is equal to or exceeds the dispatched value forthe current time frame, a signal to activate is generated; otherwise, asignal to deactivate is generated).

Any combination or subcombination of the disclosed method acts can berepeated after a fixed period of time (e.g., a time period of 15 minutesor less, 5 minutes or less, or other such time period). In certainembodiments, some of the received values are reused for subsequent timeframes. For example, the user-selected performance value and usertolerance value can be stored and reused for subsequent time frames. Insuch instances, method acts 710 and 712 need not be repeated.

FIG. 8 is a flowchart 800 showing another generalized embodiment forcomputing bids in any of the disclosed recourse allocation system usingtwo-way communications (e.g., through transactive controllers). Themethod in FIG. 8 can be performed by computing devices like thosementioned above with respect to FIG. 7. Likewise, the bids computed bythe method in FIG. 8 can be associated with electrical devices likethose mentioned above. In one particular embodiment, the electricaldevice is a pump.

At 810, an indication of a current status of a system controlled by anelectrical device is received. For example, if the electrical device isa pump, the current status of the system can be a measurement of a waterlevel affected by the pump.

At 812, a bid value for purchasing electricity sufficient to operate theelectrical device is computed. In the illustrated embodiment, the bidvalue is based at least in part on the current status of the system andon one or more additional values, which in the illustrated embodimentinclude one or more values indicative of the dispatched index valuesfrom a futures market (e.g., prices from a day-ahead market). Theadditional values can comprise, for example, any one or more of thevalues discussed above with respect to FIG. 7 (e.g., one or more ofdispatched index values from a futures market, dispatched index valuesfrom the local market, averages of the dispatched index values from thefutures market and/or the local market, or standard deviations of thedispatched index values from the futures market and/or the localmarket). Any of these values can be computed locally at the electricaldevice, or can be transmitted from the central computer. In someembodiments, a user comfort setting and/or desired performance levelselected by a user (e.g., a user comfort setting and/or desiredperformance level as explained above with respect to FIG. 7) is alsoreceived. In these embodiments, the bid value is additionally based atleast in part on the user comfort setting.

At 814, the bid value is transmitted to one or more central computers(e.g., one or more computers that manage and operate a local resourceallocation market by, for instance, conducting the auction process forthe market) in the market-based resource allocation system (e.g., usingsuitable communication means, such as the Internet or other network). Incertain embodiments, the quantity of the resources desired (described,for example, as a rate of consumption for the time frame over which theresource will be allocated) is also transmitted to the one or morecentral computers. Additionally, in some embodiments, the current stateof the electrical device associated with the bid is also transmitted tothe one or more central computers.

At 816, an indication of a dispatched value for a current (or upcoming)time frame is received from the one or more central computers. Incertain embodiments, a value other than the dispatched value but whichis indicative of the dispatched value is received. For example, thevalue can indicate a difference between a last dispatched value and anewly dispatched value. As noted, the one or more central computers cantransmit other information as well (e.g., one or more of dispatchedindex value(s) from a futures market, averages of the dispatched indexvalues from the futures market and/or the local market, or standarddeviations of the dispatched index values from the futures market and/orthe local market).

At 818, the bid value is compared to the dispatched value for thecurrent (or upcoming) time frame, and a signal is generated to activateor deactivate the electrical device based on this comparison (e.g., ifthe bid value is equal to or exceeds the dispatched value for thecurrent time frame, a signal to activate is generated; otherwise, asignal to deactivate is generated).

Any combination or subcombination of the disclosed method acts can berepeated after a fixed period of time (e.g., a time period of 15 minutesor less, 5 minutes or less, or other such time period). In certainembodiments, some of the received values are reused for subsequent timeframes. For example, the user comfort setting can be stored and reusedfor subsequent time frames.

FIG. 9 is a flowchart 900 showing a general embodiment for computingbids in any of the disclosed recourse allocation system using one-waycommunications (e.g., using an active controller). The particular methodshown in FIG. 9 is for an electrical device in a system for allocatingelectrical resources, but this usage should not be construed aslimiting. The electrical device can be a variety of devices, such as anair-conditioning unit; heating unit; heating, ventilation, and airconditioning (“HVAC”) system; hot water heater; refrigerator; dishwasher; washing machine; dryer; oven; microwave oven; pump; homelighting system; electrical charger; electric vehicle charger; homeelectrical system; or any other electrical system having variableperformance states. The method of FIG. 9 can be performed usingcomputing hardware (e.g., a computer processor or an integratedcircuit). For instance, the method can be performed by a computer at anend-user's locale or home, a computer coupled to an electrical device,or by specialized hardware (e.g., an ASIC or programmable logic device)coupled to the electrical device. Furthermore, it should be understoodthat the method acts in FIG. 9 do not necessarily occur in theillustrated sequence.

At 910, one or more user tolerance setting selected by a user isreceived. For example, a user comfort setting as described above withrespect to FIG. 7 can be received. The user tolerance value can be inputby the user, for example, through an appropriate graphical userinterface displayed on a computer or through a keypad, touch screen,dial, or other control mechanism associated with the electrical device.

At 912, market information is received. For example, the marketinformation can be transmitted from the one or more central computers.In the illustrated embodiment, the market information includes a valueindicative of the dispatched index value from a futures market (e.g.,the price from a day-ahead market). The market information can furtherinclude information about the dispatched index value for the localmarket (e.g., the price in the local market for the most recent timeperiod or the next upcoming time period).

At 914, a probability value of operating the electrical device iscomputed based on at least the user comfort setting and one or moreadditional values. The additional values can comprise, for example, anyone or more of the values discussed above with respect to FIG. 7 (e.g.,one or more of the average and/or standard deviation of historicaldispatch values of the local market, historical dispatch values fromother levels or markets within the resource allocation system, and/orthe dispatch values of a futures market). Any of these values can becomputed locally at the electrical device, or can be transmitted fromthe one or more central computers.

At 916, a determination is made as to whether to operate the electricaldevice using the probability value, and a signal is generated to causethe electrical device to operate based on this determination. In someimplementations, for instance, a random number is generated, which iscompared to the probability value. If the random number is less than (orin some embodiment greater than) the probability value, then the signalfor causing the electrical device to operate is generated; otherwise, asignal for causing the electrical device to deactivate can be generated.

Any combination or subcombination of the disclosed method acts can berepeated after a fixed period of time (e.g., a time period of 15 minutesor less, 5 minutes or less, or other such time period). In certainembodiments, some of the values are reused for subsequent time frames.For example, the user comfort setting can be stored and reused forsubsequent time frames.

H. Supply Strategies

Suppliers can consider many factors when computing their offer indexvalue. For example, if there is a production start-up cost, it can bespread over a minimum of M time frames using the formula:

$\begin{matrix}{{index} = {{variable} + \frac{startup}{M \cdot {capacity}}}} & (1)\end{matrix}$where index is the index value of the offer, variable is thetime-dependent index value, startup is the index value of startingproduction, and capacity is the total production capacity of the unit.The variable term can correspond to or be computed from the average ofone or more dispatched index values over N previous time frames (e.g.,any one or more of the dispatched index values described above withrespect to FIG. 7, including the dispatched index values from thefutures market and/or the dispatched index values from the localmarket). In certain embodiments, the variable term is also based atleast in part on the standard deviation of those dispatched indexvalues. As noted above with respect to FIG. 7 other derivative valuescan also be used to compute the variable term. The variable term canhave any of the values of the average price P_(average) described belowin Section V.

Similarly, a supplier for which production is already engaged can adjusttheir offer strategy by lowering their offer's index when they wish toassure their resource is used for a minimum number of time frames. Inorder to compensate for the potential lost returns during those minimumrun time periods, suppliers can increase their initial start-up indexenough to offset for potential losses. Here again, one approach is forresource producers to use the average and/or standard deviation ofpreviously dispatched index values (e.g., any one or more of thedispatched index values described above with respect to FIG. 7,including the dispatched index values from the futures market and/or thedispatched index values from the local market) to forecast the mostlikely variations and use an increment (M) that minimizes the potentialloss over the desired minimum run time:

$\begin{matrix}{{index} = {{variable} - {M\frac{shutdown}{runtime}}}} & (2)\end{matrix}$where index is the offer index value, variable is the time-dependentindex value (e.g., as explained above with respect to Equation (1)),shutdown is the index value of shutting down production, and runtime isthe number of periods over which the unit has already run.

Another common objective for suppliers is that they not exceed a maximumproduction quota allotted for a number of time frames. One solution tothis problem is to adjust the offer's index price based on how much ofthe allotment has been used in relation to number of time frames thathave past. Producers that have used a disproportionately high allotmentremaining will have lower offers than those that have used adisproportionately low allotment remaining. For example, a supplier witha limited operating license can use:

$\begin{matrix}{{index} = {{variable} + {{{capacity} \cdot {fixed}}\frac{remaining}{{license} - {run}}}}} & (3)\end{matrix}$where index is the offer index value, variable is the time-dependentindex value (e.g., as explained above with respect to Equation (1)),fixed is the time-independent index value, remaining is the number oftime frames remaining unused in the license, license is the number oftime frames in the license, and run is the number of time frames used inthe license.

FIG. 10 is a flowchart 1000 showing a general embodiment for generatingoffer values for use in any of the disclosed recourse allocationsystems. The particular method shown in FIG. 10 is for an electricalresource (e.g., a generator) in a system for allocating electricalresources, but this usage should not be construed as limiting. Themethod of FIG. 10 can be performed using computing hardware (e.g., acomputer processor or a specialized integrated circuit). For instance,the method can be performed by a computer at a supplier's locale, acomputer coupled to an electrical generator, or by specialized hardware(e.g., an ASIC or programmable logic device) coupled to the electricalgenerator. Furthermore, it should be understood that the method acts inFIG. 10 do not necessarily occur in the illustrated sequence.

At 1010, an offer value indicative of a value at which electricity canbe supplied by a generator for a current (or upcoming) time frame iscomputed. In the illustrated embodiment, and as explained above, theoffer value is based at least in part on any one or more of the valuesdiscussed above with respect to FIG. 7 (e.g., any one or more of thedispatched index values described above with respect to FIG. 7,including the dispatched index values from the futures market and/or thedispatched index values from the local market). Any of these values canbe computed locally at the electrical device, or can be transmitted fromthe one or more central computers. The offer value can also beadditionally based at least in part on a startup cost for supplying theelectricity, a shutdown cost for supplying the electricity, and/or aremaining number of time frames available in an operating licenseassociated with the electricity as described above (e.g., using aweighted sum or other technique).

At 1012, the offer value is transmitted to the one or more centralcomputers (e.g., one or more computers that manage and operate a localresource allocation market by, for instance, conducting the auctionprocess for the market) along with a value indicative of a quantity ofelectricity that can be supplied by the generator during the currenttime frame (or next upcoming time frame) (e.g., using suitablecommunication means, such as the Internet or other network).

At 1014, an indication of a dispatched value for a current (or upcoming)time frame is received from the one or more central computers. Incertain embodiments, a value other than the dispatched value but whichis indicative of the dispatched value is received. For example, thevalue can indicate a difference between a last dispatched value and anewly dispatched value. As noted, the one or more central computers cantransmit other information as well (e.g., one or more of dispatchedindex value(s) from a futures market, averages of the dispatched indexvalues from the futures market and/or the local market, or standarddeviations of the dispatched index values from the futures market and/orthe local market).

At 1016, the dispatched value is compared to the offer value, and thegenerator is activated in response to the comparison.

Any combination or subcombination of the disclosed method acts can berepeated after a fixed period of time (e.g., a time period of 15 minutesor less, 5 minutes or less, or other such time period). In certainembodiments, some of the values are reused for subsequent time frames.Furthermore, there are instances when the offer value is used as a bidin the market-based resource allocation system. For example, when theelectrical resource is configured with an emergency transfer switch forsupplying particular consumers in the power grid, then the offer valuecan be used as a bid value along with a value indicative of a magnitudeof electrical load the generator can remove from the power grid.

I. Ramp Rates

Some resources cannot change their production or consumption output morethan a certain amount within a single time frame. In this case, theresource being offered is not the quantity, but the change in quantity.This situation can be handled by treating the change in quantity as adistinct resource rather than as an extra feature of an existingresource. This way, resources for which ramp rates apply have an extraresource allocation strategy, which can be handled separately andindependently. This strategy also helps maintain the independence ofeach resource as regards its allocation.

J. Multiple Resources

Each consumer and producer can engage in both demand and supply of anynumber of resources. For example, a producer may offer to supply aquantity of X at index A, while simultaneously requesting a quantity ofY at index B. If the producer depends on having Yin order to produce X,it addresses the risk of losing access to Y while still having toproduce X by either adjusting the offer and request indexes, or ensuringthat it has an alternate source for Y or is ready to pay the penalty fornot delivering X. The same considerations apply for consumers.

K. Effect of Constraints

Frequently situations arise where a resource that is available in onepart of a service provider's system cannot be delivered in its entiretyto another part of the same system. Such delivery constraints can beaddressed by segregating the system into two separate resourceallocation systems that operate independently. For example, the systemwith surplus resources can make a supply offer into the system with adeficit, and the system with a deficit can make a consumption requestfrom the surplus system. Each system can dispatch its own index value,in which case the index difference will represent the impact of theconstraint on both systems. In some embodiments, the aggregator resourceallocation system can credit a capacity expansion account, which is usedto support the improvement of the connection between the two such thatthe constraint is eventually addressed.

L. Capacity Management Market

The resource allocation system described above can also be operated as acapacity management market. A capacity management market can be viewedas a special case of the transactive market that attempts to managecongestion in its local market (e.g., a market at the distributionfeeder level). The congestion limit may be caused by local conditions,such as thermal conductor ratings, or for higher level reasons, such asthe reduction of localized congestion on sub-transmission networks, orany combination of the above. In the discussion below, the congestionlimit is illustrated as being applied at the feeder level to reduce peakdemand, though this usage should not be construed as limiting.

The transactive controllers described herein deal with bidding the priceand quantity of a load into the auction, termed the “responsive” load.However, the supplier bids and the non-bidding loads on the system (suchas lights, refrigerators, or losses) are also desirably accounted forand bid into the auction. The non-bidding loads are called“unresponsive” loads, because their demand does not change as a functionof price. In certain embodiments, the operator of the capacitymanagement market fills the role of supply bidder, while also accountingfor the unresponsive loads. To bid the supplier curve, the operator ofthe capacity management market bids the congestion limit at theuncongested market price. FIG. 11 is a graph 1100 illustrating theresulting supply curve that is formed. In particular, FIG. 11 shows thatwhen the congestion is bid and forms the only bid in the market, avertical line 1110 is formed at the congestion quantity to the price capof the market. In general, the uncongested market price is the bulk costof electricity plus mark-up, such as the Locational Marginal Price(“LMP”), or any other appropriate price signal, such as direct purchaseof power from distributed generators.

Another typical objective of the operator of the capacity managementmarket is to estimate the total amount of load on the feeder and thendetermine what portion of the load is responsive versus unresponsive tothe price signal. The total load can be measured directly from theequipment requiring the capacity limit, such as a substationtransformer. The currently responsive load can be determined from thecontrol bids received from transactive controllers. In particularembodiments, for example, the transactive controllers transmit theircurrent operational state at the market intervals in addition to theother information associated with their demand bids. In one embodiment,the currently responsive load is determined by summing the quantity ofthe demand currently in an active (or “ON”) state as follows:

$\begin{matrix}{{Q_{resp} = {\sum\limits_{1}^{N}\;{Q_{{bid} - N}*{state}}}},} & (4)\end{matrix}$where state equals “0” if the electronic device is OFF, and “1” if theelectronic device is ON, and N is the total number of submitted bids.The unresponsive load is then determined by subtracting the responsiveload from the total load. The unresponsive load quantity is then bidinto the market at the market price cap (or some other value that isgreater than the other bids) by the operator of the capacity managementmarket, as shown by line 1120 in FIG. 11.

During periods of time when the total load is less than the capacitylimit, the electronic devices will respond by adjusting their setpointsto the current market price. However, when the total load tries toexceed the capacity limit, the capacity management market will increasethe price of electricity to limit the quantity at the capacity limit,effectively reducing the total load to within the constrained limits.

M. Exemplary Market Clearing Scenarios

Any one or more of the exemplary market clearing scenarios describedbelow can be utilized or experienced in embodiments of the resourceallocation systems described herein. For example, the one or morecentral computers used to manage and operate a local resource allocationmarket can determine the dispatched index or market clearing price usingany of the techniques described below, depending on the bidding scenariopresented.

Market clearing begins with the sorting of both buying and sellingcomponents. In certain embodiments, buyers are sorted from highest priceto lowest price. Sellers are sorted from lowest price to highest price.In certain embodiments, buyer and seller curves are then created by thecumulative sum of the quantities associated with these sorted prices. Inparticular implementations, the curves are implemented ascomputer-usable representations of the curves that are computed andstored once the necessary input data is received. The representations ofthe curves can comprise, for example, arrays of values or othercomputer-usable data elements or structures. The two, sorted curves canthen be overlaid or otherwise analyzed to determine an intersectionbetween the curves. For example, each curve can be considered to movefrom one extreme in price to the other (e.g., the buyer curve willtraverse from a +∞ price to a −∞ price, and the seller curve willtraverse from a −∞ price to a +∞ price. In FIGS. 13-23 discussed below,these extremes occur at the zero-quantity location and after all selleror bidder quantities are bid (right edge of the bidding curve)).

In general, the market clears at the intersection of the buying andselling curves of the market. According to certain embodiments of thedisclosed technology, for a discrete bidding scenario, four distinct,valid clearing situations are supported. The resulting clearing priceand quantity are slightly different for each scenario and are desirablyhandled appropriately. Two more invalid, or failure, scenarios are alsopossible. For each scenario presented in FIGS. 13-23, buyers arerepresented in the descending curve and sellers are represented in theascending curve, and the final market clearing point is designated by acircle or point. There are five distinct buyers and four distinctsellers in each of the four, valid market clearing examples. The twoother clearing scenarios (failure and null) follow the same convention,but may have different numbers of buyers and sellers.

The first example market clearing scenario is shown in graph 1300 ofFIG. 13. The buyer bids have been sorted by descending price, and theseller bids have been sorted by ascending price. In this scenario, theintersection point of the buying and selling bid lines is at the edge ofa buyer quantity, but not at the edge of a seller quantity. When thishappens, the partially accepted seller is considered the marginalseller. In this example, the clearing price of the market is thismarginal seller's bid price, and the clearing quantity is the sum of allof the buyers' quantities for which the bid was higher than the marginalseller's bid price.

In the market clearing portrayed in FIG. 13, three buyers submitted bidsat or above the clearing price. Three sellers submitted bids at or belowthe clearing price. The three buyers accept the market clearing priceand purchase their desired quantities from the market at the clearingprice. The two lowest cost sellers sell their bid quantities into themarket at the clearing price. The third seller becomes the marginalseller. It gets the price of the market, but the market only needs partof its bid quantity. The marginal seller will only produce a partialoutput, and may need to track the specific requirements of the system.

In the context of an electrical power market, the marginal seller wouldbe the marginal generator. As the load varies a little bit over thecurrent market interval, the marginal generator will adjust its outputto match the demand of the system. Utilizing frequency as the metric fortracking, the generator could adjust its output appropriately. If theload increased, the frequency would decrease and the marginal generatorwould need to increase its output. If the load decreases, the frequencywill increase and the marginal generator needs to reduce its output.

A second example market clearing scenario is shown in graph 1400 of FIG.14. This scenario is the converse of the first scenario. Here, the buyerand seller curves intersect at an edge of a seller curve, but not at theedge of a buyer quantity. Similar to the marginal seller condition, thebuyer is now referred to as the marginal buyer. The market clearingprice is set at the bid price of this marginal buyer. In this example,the market clearing quantity is set to the sum of the quantities of thesellers whose bids are less than the marginal buyer's price.

As with the previous example, three buyers submitted bids at or abovethe clearing price. However, only two sellers submitted bids at or belowthe clearing price. In this example, the two sellers accept the clearingprice of the market, and both sell their full quantities for the marketclearing price. The first two buyers are satisfied with the market, sothey buy their full desired quantity at the clearing price. The thirdbuyer gets the price of the market, but there is not enough supply tomeet its demand. As a result, a proportional response (or no response)will be necessary to meet the clearing criterion.

In the context of an electrical power market, there are a couple ofpossible methods for handling the marginal buyer quantity. The firstmethod is to consider the marginal buyer to be a device capable ofproportional output, like a battery charger. Rather than requiring a100% charging rate, it will provide a proportional amount of that rate.Unfortunately, this method typically only works if the marginal quantityis a single object. If the marginal quantity is actually made up ofseveral buyers (at the same price), this strategy becomes moredifficult. For instance, if the marginal quantity is made up of severalbuyers, the buyers desirably regulate themselves independently such thatin the aggregate they do not exceed the clear quantity. This can bedone, for example, by throttling the buyers individually to the fractionof the marginal quantity actually cleared, or by using a random number(e.g., a Bernoulli distributed random number) to determine whether eachunit should run during the next interval.

An alternative is to use a method similar to the marginal generator. Forexample, the marginal loads can all monitor the system's frequency.Utilizing a controller that monitors grid frequency and adapts inresponse to the frequency (e.g., a grid friendly appliance controller(“GFA”)), loads can adjust accordingly. If the frequency drops, it meansthe marginal load is too great, so each marginal load would reduce itsoutput. If the frequency increases, the marginal load is too small andneeds to increase to balance out the fixed generation. Marginally loadcontrolled by a GFA controller is basically the converse of thegenerator case.

A variation on the second marginal buyer in power systems method stillutilizes a device like the GFA. However, rather than only the marginalload arming the GFA functionality, all GFAs in the system are armed. Theregulation price of the system is set to the marginal clearing price.Any device that exercises its GFA functionality during this time willreceive compensation at the regulation price. This method helps increasethe pool of marginal load since devices below the clearing threshold maystill elect to participate in this regulation market.

Graph 1500 of FIG. 15 represents a third market clearing example. Here,the intersection of buyer and seller curves is at the same quantity, butat different prices. The intersection point occurs on an edge of boththe buyer and seller quantity curves. In this example, the quantity isset to the intersection point of the two curves. This quantity is eitherthe sum of all sellers' quantities with a bid less than the “marginal”seller bid, or the sum of all buyers' quantities with a bid higher thanthe “marginal” buyer bid. However, in some embodiments, the price isdesirably taken as the average price of the two intersecting bids. Forexample, the clearing price can be determined as:

$\begin{matrix}{{Price}_{Clear} = {\frac{\left( {{Bid}_{Seller} + {Bid}_{Buyer}} \right)}{2}.}} & (5)\end{matrix}$

In the market scenario of FIG. 15, three buyers submitted bids at orabove the clearing price of the market, and three sellers submitted bidsat or below the clearing price. In this case, all three buyers' and allthree sellers' bidding quantities are met (since the cumulative sum ofthe cleared bids is the same for both buyers and sellers). Due to theunique nature of this clearing condition, a compromise is made betweenthe lowest cleared buyer bid and highest cleared seller bid. In thisexample, the final clearing price is not set to either of their values,as was the case with the marginal buyer or marginal seller scenariosabove. Rather, the clearing price is set as the average of the clearedbuyer and seller bids. This allows both lower bid sellers and higher bidbuyers to respond. However, it prevents the subsequent buyer and sellerquantities from meeting the clearing price.

The scenario of FIG. 15 is not always as straight forward. Graph 1600 ofFIG. 16 shows a scenario where the next seller bid also meets thedesired clearing point. Graph 1700 of FIG. 17 shows the same scenariofrom a buyer perspective. In order to help ensure the proper biddersrespond to the cleared market, the clearing price are desirably adjustedaccordingly. For example, the final clearing price can be furtherconstrained by the next buying and selling sorted bids. The finalclearing price can be selected as the point that is closest to the idealclearing price, but ensures the subsequent buyer and seller bids are notvalid clearing conditions of the market. The maximum and minimum of thisfurther constraint are determined by the specific bidding scenario.

In graph 1600 of FIG. 16, the next seller bid does not exceed the idealclearing price. To ensure proper operation and that this device does notactivate, the price associated with this bid is desirably above thefinal clearing price. This “next” bid price serves as a non-inclusivemaximum for the clearing price. Therefore, the clearing price will haveto be slightly below this “next” seller bid. Typically, this will be$0.01 below the next seller bid price.

Graph 1700 for FIG. 17 shows a similar behavior with buyer bids. Thenext buyer bid does not fall below the ideal clearing price. Therefore,this device would assume it was the marginal quantity and activate. Toprevent this, the final clearing price is desirably set slightly higherthan this bid. In a manner similar to the previous seller curve example,the “next” buyer bid serves as a limit for the clearing price. However,in this scenario, the “next” buyer bid serves as a non-inclusive lowerlimit for the market clearing price. The final clearing price of themarket must then be slightly above this “next” buyer bid. Again, thismay be on the order of a $0.01 increase (or other small incrementalamount) above the next buyer bid.

Another valid scenario is actually a variation on the scenario shown inFIG. 15. Graph 1800 of FIG. 18 is an example showing the situation whenboth the buyer and seller clearing quantities and clearing prices matchexactly. In this case, the clearing price is the intersecting price(common for both buyer and seller). The clearing quantity is the sum ofall buyers with bid prices equal to or greater than the clearing price,or the sum of all sellers with bid prices equal to or lower than theclearing price. Approaching the clearing point from either the seller orbuyer curves should yield identical results.

As indicated, this scenario is a variation on the example shown in FIG.15. Three buyers and three sellers are satisfied by the market clearingcriterion. The clearing buyer and seller both agree on an exact price,so the clearing price is easy to determine. The cumulative sum of allbuyers whose bid was greater than or equal to the clearing price isequivalent to the cumulative sum of all sellers whose bid was less thanor equal to the clearing price.

A fourth scenario for the market is for the market to fail to clear.This will happen when there is insufficient supply to meet the highestbid-price buyer. Graph 1900 of FIG. 19 provides a common example of thiscondition. In the full market scenario, there may be buyers on thesystem that are not participating in the market. These “unresponsivebuyers” need to be satisfied before any bidding buyers are handled. Inthe market, the quantity requested by these devices (estimated or known)is bid into the market at the price cap. This ensures it is met by theseller curve before any responsive loads are considered. In anelectrical power market example, these unresponsive buyers would beend-use devices that are not submitting bids into the market, or losseson the distribution system. The market must cover and serve theunresponsive loads and losses before it can handle responsive devices.

In the scenario of FIG. 19, all seller quantities fail to meet thehighest buyer bid requirements (typically due to an unresponsive buyer).In such a scenario, the market simply fails to clear. The intersectionpoint of this market will occur at the total seller quantity and theprice cap. This is effectively a marginal buyer condition of FIG. 14.

Another clearing scenario is in a similar category to the market failureof FIG. 19. On the other end of the spectrum, suppose the buyers andsellers do not agree on a bid price at all. FIG. 20 includes graph 2000illustrating this scenario, the Null market clearing scenario. As thebuyer and seller curves demonstrate, there is no intersection point foran agreeable price and quantity. The lowest priced seller is higher thanthe highest priced buyer.

In the null market scenario, the price poses a problem. If set to theprice cap, no buyers would activate, but all sellers would activate. Ifset to 0 or the negative price cap, all buyers would activate, but nosellers would be present to meet the demand. A price signal to keep bothparties from responding is between the highest buyer bid and the lowestseller bid. A variation on the equation from the third scenario above isto compute an average price, given as:

$\begin{matrix}{{Price}_{Clear} = \frac{\left( {{Bid}_{LowestSeller} + {Bid}_{HighestBuyer}} \right)}{2}} & (6)\end{matrix}$

The resulting price is higher than any buyer, so no buyers will beactivated. The price is lower than any seller, so none of the sellerswill be activated. The market desirably includes an overall indicatorthat indicates a market clearing failure. However, by setting the priceto a value that satisfies neither buyers nor sellers in the market,responses to a failed market should be mitigated.

According to certain embodiments of the disclosed technology, the marketcan only clear in one of these scenarios. Some more complex clearingsituations can occur, but they are typically related to the base casesintroduced above. Graph 2100 in FIG. 21 demonstrates a variation on themarginal seller case of FIG. 13. Despite the intersecting bids havingthe same price, this scenario can be treated as identical to FIG. 13.The three highest buyer bids are satisfied by the market and thesebuyers purchase their desired quantities. Two sellers are below theclearing price, so they accept the market price and sell their fullquantities into the system. The third seller is at the clearing price,but its full quantity is not needed. This seller gets the clearing priceand acts as the marginal seller in the market. It will only provide partof its full output, and may need to track the buyer demand around thatoutput point.

Graph 2200 in FIG. 22 shows a similar example where the clearing priceis again clear. However, the quantities once again do not properlyalign. This case is merely a special case of the scenario presented inFIG. 14, or a marginal buyer scenario. The clearing price of the marketis clear. Only the sellers with bids at or below the clearing priceaccept the market clearing value, and only buyers with bids at or higherthan the clearing price accept the cleared market. Unfortunately, thereis more demand from the buyers than there is supply from the sellers. Assuch, the buyer with the market clearing bid will not be able to havetheir full bid quantity satisfied. This buyer will consume only theappropriate portion of their bid quantity.

If the capability for unresponsive buyers is included, that scenariodesirably factors into the market clearing as well. Once theunresponsive load quantity is known or estimated, it is bid into themarket as a buyer quantity with a large price (e.g., the price cap).Graph 2300 of FIG. 23 shows how unresponsive buyers fit into the “fivebidder, four seller” example for the four valid market clearingscenarios. The unresponsive buyers effectively become a bidding quantitythat is always met first. Only once the needs of these unresponsivebuyers are satisfied, will responsive buyer devices be able to interactwith the market. If the needs of the unresponsive buyers are greaterthan the seller supply, the market failure case of FIG. 19 will occur.

A variation on FIG. 23 and FIG. 19 occurs between the two conditions. Acase in which the available seller quantities only meet the unresponsivebidder demand is illustrated in graph 2400 of FIG. 24. Desirably, theclearing price in this scenario would be the price cap of the system.This would enable all of the seller quantities, and only theunresponsive bidder quantity. However, such a “price cap bid” istypically significantly larger than other bids on the system. Under sucha scenario, this bid would severely skew the output statistics on themarket. To prevent this large skew, the clearing price is adjustedslightly. Similar to the conditions in FIG. 16 and FIG. 17, the finalclearing price desirably still ensures all sellers are producing, butonly the unresponsive buyers are consuming. As such, the marginalseller's price becomes the non-inclusive lower limit of the clearingprice. The clearing price will then be set slightly above this price.This will help ensure that all sellers on the system are activated, butno responsive buyers meet the price criterion.

IV. Detailed Exemplary Implementations of the Resource Allocation SchemeUsing Dispatched Values from Futures Markets

The following are examples of end-use and distributed resource controltechniques that can be used in the general resource allocation schemedescribed above in Section III and illustrated in FIG. 3.

A. Two-Way, Transactive Control for Thermostatically-ControlledEquipment

The techniques introduced in this section can be applied to transactivecontrollers for thermostatically controlled equipment. The transactivecontrollers are capable of transmitting bids to the resource allocationsystem. For illustrative purposes, the control is assumed to be forspace or water heating and cooling (e.g., in residential or commercialbuildings). The approach can be extended to other contexts as well(e.g., refrigerator; dish washer; washing machine; dryer; oven;microwave oven; pump; home lighting system; electrical charger; electricvehicle charger; home electrical system; or any other electrical systemhaving variable performance states).

In one exemplary embodiment, thermostatically controlled heating andcooling modifies conventional controls by explicitly using marketinformation obtained through interaction with a resource allocationsystem, such as any of the resource allocation systems introduced abovein Section II and III. In particular, the exemplary embodiment uses bidand dispatched index value information. In the discussion below, bidsand dispatched index values are sometimes referred to in terms of a costor price. It is to be understood that this “cost” or “price” canrepresent an actual monetary cost or price, or an index value in termsof the relevant resource allocation index. Furthermore, the dispatchedindex value from the resource allocation system is sometimes referred toherein as the “clearing price”.

A bid curve can be used to functionally relate the cost of a service toa user's comfort. FIG. 12 shows a diagram 1200 of an exemplary bid curvethat graphically illustrates several of the concepts embodied in thebidding technique described below. The exemplary bid curve in diagram1200 is derived from the mean and standard deviation of dispatchedvalues from a futures market (e.g., dispatched values of a day-aheadmarket for electricity over a 24-hour period) along with exemplaryminimum and maximum temperature limits that result from a comfortsetting selected by a user. Desirably, the standard deviation andaverage of the dispatched values from the futures markets arecontinually evaluated and updated (e.g., periodically as new dispatchedvalues become available).

In the exemplary technique described below, the occupant of a locale orzone that is thermostatically controlled (the user, in this example)provides one or more inputs. For instance, the occupant selects apreferred temperature setting T_(set) for each scheduled occupancyperiod. For each occupancy period, the occupant also selects one or morecomfort settings from among a set of alternatives. In one exemplaryembodiment, a comfort setting is associated with pairings of elasticityfactors and temperature limits (in the illustrated example: k_(T) _(—)_(L), T_(min) and k_(T) _(—) _(H), T_(max)), such as those listed inTable 1, or any combination thereof. In other embodiments, thetemperature limits T_(min) and T_(max) can be individually selected bythe user, and the values k_(T) _(—) _(L) and k_(T) _(—) _(H) can bederived from the selected values. In still other embodiments, theelasticity factors can also be individually selected by the user.

TABLE 1 Exemplary Comfort Settings One-way Thermostatic 2-Way ControllerController Comfort Cooling Heating Comfort settings k_(w) Settingk_(T)_L/k_(T)_H Tmin/Tmax k_(T)_L/k_(T)_H Tmin/Tmax maximum economy 2.0maximum economy 1/1   0/10^((b)) 1/1 −10/0^((b)) balanced economy 1.5balanced economy 2/2   0/10 2/2 −10/0 balanced 1.0 comfortable economy3/3   0/10 3/3 −10/0 balanced comfort 0.5 economical comfort 1/1   0/51/1  −5/0 maximum comfort 0.0 balanced comfort 2/2   0/5 2/2  −5/0maximum comfort 3/3   0/5 3/3  −5/0 maximum economy^((a)) 1/1 −3/10 1/1−10/3 balanced economy^((a)) 2/2 −3/10 2/2 −10/3 comfortable 3/3 −3/103/3 −10/3 economical comfort^((a)) 1/1 −3/5 1/1  −5/3 balancedcomfort^((a)) 2/2 −3/5 2/2  −5/3 maximum comfort^((a)) 3/3 −3/5 3/3 −5/3 no price reaction ∞/∞   0/0 ∞/∞    0/0 ^((a))with pre-heat andpre-cool option. ^((b))T_(min) and T_(max) are expressed as ° F. aboveor below the present

In the example illustrated by diagram 700 in FIG. 7, thehigh-temperature limit T_(max) corresponds to k_(T) _(—) _(H) standarddeviations from the mean price. Furthermore, in this example, the valuek_(T) _(—) _(H) is automatically determined with the user's selectedcomfort setting (e.g., using a look-up table or file storing theelasticity factors and temperature limits associated with each comfortsetting, or computing the values upon user selection of the temperaturelimits). The values of k_(T) _(—) _(L) and k_(T) _(—) _(H) are notnecessarily identical for the upper and lower parts of the bid curve.Further, in this example, if both k_(T) _(—) _(H) and k_(T) _(—) _(L)are sufficiently (or infinitely) large, the thermostat will functionlike a normal thermostat unaffected by grid conditions and the behaviorsof the market. With such a configuration, the thermostat is said to beinelastic.

The example shown in FIG. 12 is one in which it is desired to cool thecurrent temperature (e.g., it is an example in which electricalresources are to be used for air conditioning). In the example, a highvalue of k_(T) _(—) _(H) will lead to relatively high bids when the zonetemperature exceeds the desired zone temperature T_(set), which a highbid will help make sure that the zone cooling bid will win the right tobecome satisfied. If k_(T) _(—) _(L) and k_(T) _(—) _(H) are small(representing elastic behavior), bids from the thermostaticallycontrolled load deviate little from the mean price, and currenttemperatures are permitted to vary throughout a relatively largetemperature range (from T_(min) to T_(max)) as the market's clearedprice changes.

The following discussion describes one exemplary technique for computinga bid value for a current market interval or next upcoming marketinterval. In the exemplary technique, the current indoor zonetemperature T_(current) can be determined and the consequent bid priceP_(bid) computed. According to the exemplary technique, the bid price isbased on the slope of the bid curve and the difference between thecurrent zone temperature T_(current) and the desired zone temperatureset point T_(set). The corresponding bid price depends on additionalparameters that are determined from the user's one or more selectedcomfort settings and that are indicative of the user's willingness totolerate differences in temperature from the desired temperaturesetting. In this example, the additional parameters comprise k_(T) _(—)_(L), k_(T) _(—) _(H), T_(max) and T_(min). The bid price P_(bid) isalso based at least in part on an average price. In certain embodiments,the average price P_(average) is based at least in part on one or morevalues indicative of dispatched levels from a futures market. Inparticular embodiments, the following equation can be used:

$\begin{matrix}{P_{bid} = {P_{average} + {\left( {T_{current} - T_{set}} \right)\frac{k_{T} \times \sigma_{actual}}{{T_{limit} - T_{set}}}}}} & (7)\end{matrix}$where P_(bid) is the bid price, P_(average) is the average price ofelectricity over a set interval and can be computed using one of themethods described below in Section IV (the value P_(average) issometimes also referred to as the “expected price” since it indicates alikely price), σ_(actual) is the standard deviation of the electricityprice for the same period and can be computed using one of the methodsdescribed below in Section IV, k_(T) is one of k_(T) _(—) _(L) or k_(T)_(—) _(H) depending on where T_(current) presently resides on the bidcurve, and T_(limit) is one of T_(min) or T_(max), depending on whereT_(current) presently resides on the bid curve. For example, ifT_(current) is above T_(set) then k_(T) is k_(T) _(—) _(L) and T_(limit)is T_(min), but if T_(current) is below T_(set) then k_(T) is k_(T) _(—)_(H) and T_(limit) is T_(max). The use of an average price based onrecent dispatched values (e.g., recently dispatched values from afutures market) and a recent standard deviation enables the technique toautomatically adapt and scale a bid value so that it is competitive inthe present market while meeting the consumer's objective with respectto comfort and economy.

The resulting bid value can then be posted to the market (e.g., theresulting bid value can be transmitted to the one or more of centralcomputers responsible for determining the clearing price in the localresource allocation system). The market then establishes the marketclearing price using this and the other bids and offers, as has beendescribed in Section III. Depending on how the resource allocationsystem is implemented, the market can be cleared externally (e.g., by aresource distributor that is external to the entity placing the bid andthat operates the central computers used to determine the dispatch price(e.g., using the exemplary method shown in FIG. 6) or internally (e.g.,by a resource distributor that is internal to the entity placing thebid).

After receiving the resulting posted market clearing price, an adjustedzone set point T_(set,a) can be calculated. For example, in oneexemplary embodiment, the following equation can be used:

$\begin{matrix}{T_{{set},a} = {T_{set} + {\left( {P_{clear} - P_{average}} \right)\frac{{T_{limit} - T_{set}}}{k_{T} \times \sigma_{actual}}}}} & (8)\end{matrix}$where P_(clear) is the posted market clearing price (or dispatchedprice), P_(average) is the average price of electricity over a setinterval and can be computed using one of the methods described below inSection V, σ_(actual) is the standard deviation of the electricity pricefor the same period and can be computed using one of the methodsdescribed below in Section V, k_(T) is one of k_(T) _(—) _(L) or k_(T)_(—) _(H) depending on where T_(current) presently resides on the bidcurve, and T_(limit) is one of T_(min) or T_(max), depending on whereT_(current) presently resides on the bid curve as described above.

A graphical interpretation of the adjusted set point T_(set,a) is shownin diagram 1200 of FIG. 12. The thermostat's zone set point can beadjusted to the new adjusted zone set point. Once the set point isadjusted, the thermostat's conventional control takes over.

In diagram 1200 of FIG. 12, the adjusted cooling set point falls belowthe current temperature T_(current), meaning that there presently existsan opportunity to cool the given space at an acceptable energy cost.This process continues for each market clearing cycle. The notion of asingle zone temperature set point no longer exists because the set pointcan be affected by the market as well as a user's selected comfortsetting. Note that T_(set,a) can be higher or lower than the desired setpoint T_(set) based on the market clearing price. In cooling mode,lowering the adjusted set point T_(set) below the desired set point willincrease the energy consumption as one takes advantage of low energycosts.

In general, transactive control can support more aggressive pre-coolingand pre-heating functions. (For example, lowering the set point belowwhat would normally be comfortable is done to pre-cool.) For somedynamic rate structures (e.g., time-of-use), the future price is known apriori; however, in the case of real-time pricing, the future price isunknown or highly uncertain. To pre-heat or pre-cool with real-timepricing, one should have the ability to forecast future prices. Incertain embodiments, prices from futures markets are used (as shown, forexample, in Section V below) thus creating greater opportunity forpre-cooling and pre-heating.

B. Demand Response On/Off Control for Equipment with One-WayCommunication

This section describes techniques that can be used to control equipmentthat is not capable of computing and transmitting bids to the resourceallocation system but that nonetheless can benefit from adaptive controlstrategies. Controllers using such techniques are sometimes referred toas “active controllers.” The exemplary techniques are described in thecontext of controlling a water heater, though the techniques can beapplied to a wide variety of electrical devices in which one-waycommunication is used (e.g., pool pumps, battery chargers, and thelike). These devices having one-way communication capability can beadapted to opportunistically respond to market prices without having toformulate and submit any bids.

According to one exemplary embodiment, a probability function can beused to control whether the electrical device should run during a giventime interval. For instance, the probability function can be used togrant the electrical device (e.g., a water heater) a probabilisticopportunity to run that is dependent on the relative magnitude of acleared market price.

For example, in one particular implementation, control of the electricaldevice is modified so that the signal activating the device isinterrupted with increasing likelihood as the clearing price exceeds theaverage electricity price (e.g., the average price as computed below inSection V). The greater the difference between the clearing price andthe average price, the more likely the electrical device (e.g., thewater heater circuit) will be interrupted.

In certain implementations, the user of the electrical device havingone-way communications can select one or more comfort settings fromamong multiple possible comfort settings. In one example, a comfortsetting is associated with a consequent weighting factor (in theexemplary implementation, the k_(W) factor), which either attenuates oramplifies the effect of the probability function.

A variety of probability functions can be used to control the electricaldevice, but in one particular embodiment, the following equation isused:

$\begin{matrix}{{{r = {{k_{W}\left\lbrack {{\frac{1}{\sqrt{2\pi}\sigma_{actual}}{\int_{- \infty}^{P_{clear}}{{\mathbb{e}}^{- \frac{{({P_{average} - x})}^{2}}{2\sigma_{actual}^{2}}}\ {\mathbb{d}x}}}} - \frac{1}{2}} \right\rbrack} = {k_{W}\left\lbrack {{N\left( {P_{clear},P_{average},\sigma_{actual}} \right)} - 0.5} \right\rbrack}}};{r \geq 0}}\mspace{20mu}{{r = 0};{otherwise}}} & (9)\end{matrix}$where P_(clear) is the posted market clearing price (or dispatchedprice), P_(average) is the average price of electricity over a setinterval and can be computed using one of the methods described below inSection V, σ_(actual) is the standard deviation of the electricity pricefor the same period and can be computed using one of the methodsdescribed below in Section V, N is the cumulative normal distribution,and the factor k_(W) is defined through the user's selection of one ormore comfort settings.

In certain embodiments, the probability parameter r can then be used totest the probability of turning the electrical device off by comparingit to a uniformly generated random number between 0 and 1. For instance,if r is greater than this random number, the electrical device can becurtailed. The random number can be generated at each time interval togive each water heater an opportunity to run a fraction of the overalltime that is proportional to the curtailment probability.

C. Transactive Control System Bid/Response Strategy for Electric VehicleChargers

This section introduces exemplary methods for controlling chargers(e.g., electric vehicle chargers) in a resource allocation systemaccording to the disclosed technology. For instance, some of thedisclosed methods can be used in a two-way communication system, wherethe computing hardware associated with the charger generates bids fortransmission to the resource allocation. Other methods can be used in aone-way communication system, where the computing hardware associatedwith the charger responds to market prices and selectively activates anddeactivates the charger.

In certain embodiments of the disclosed technology, electric vehiclechargers (a) increase or decrease their bids in accordance with a user'scomfort economy setting (e.g., a user-selected value, referred to hereinas the k-value) in relation to the state-of-charge (SOC) and timeremaining to desire full-charge, and/or (b) reduce or increase therate-of-charge (ROC) based on the price cleared from the market.

In one exemplary implementation, the active bid strategy for an electricvehicle charger is based on the SOC, and the bid price is computed usingthe following:P _(bid) =P _(average) −kσ _(actual)SOC_(dev)  (10)where P_(average) is the average price of electricity over a set timeinterval and can be computed using one of the methods described below inSection V, σ_(actual) is the standard deviation of the electricity pricefor the same period and can be computed using one of the methodsdescribed below in Section V, and SOC_(dev) is the fractional deviationof the SOC from a desired SOC (SOC_(des)) with respect to minimum andmaximum limits (SOC_(min) and SOC_(max)) set by the user (e.g.,SOC_(dev)=3(SOC_(des)−SOC_(obs))/(SOC_(des)−SOC_(max)) orSOC_(dev)=3(SOC_(des)−SOC_(obs))/(SOC_(min)−SOC_(des))). In operation,and according to one exemplary embodiment, the charger can be controlledso that it is turned on when the clearing price P_(clear) is less thanor equal to P_(bid) and turned off when the clearing price exceeds thebid price.

In embodiments in which only one-way communication is possible andbidding is not possible, then a passive control strategy can be used.For example, in one particular embodiment, a strategy can be used thatalters the rate of charge as a function of price. One exemplarycomputation uses the following equation:ROC_(set)=ROC_(des)(1−kP _(dev))  (11)where ROC_(des) is the desired rate-of-charge, such that

${{ROC}_{des} = \frac{\left( {{SOC}_{final} - {SOC}_{obs}} \right)}{n_{hours}}},$k is the user's comfort economy setting, with 0<k<∞, P_(dev) is theprice deviation, such that

${P_{dev} = \frac{P_{clear} - P_{average}}{\sigma_{actual}}},$SOC_(final) is the final desired state-of-charge of the vehicle,SOC_(obs) is the current observed state-of-charge of the vehicle, andn_(hours) is the number of hours remaining before the SOC_(final) mustbe achieved.

V. Determining the Average Price and the Standard Deviation UsingDispatched Prices from Futures Markets

In this section, exemplary techniques for computing some of theparameters used in determining a demand bid and used in determining adevice's response to the dispatched price are described. In particular,this section describes different methods of determining the averageprice P_(average) of electricity, the clearing price P_(clear) and thestandard deviation σ_(actual) of the electricity price for the sameperiod. The methods described below use price information from a futuresmarket (e.g., a day-ahead market) and/or price information from thelocal real-time market. By using price information from the futuresmarket, two-way and one-way controllers can react less dramatically tovolatility that may be present in the real-time market. However, it maybe desirable to include some component from the real-time market inorder for two-way and one-way controllers to react to a dramatic butpersistent change in the price in the real-time market.

In the examples described below, the price information is determinedfrom wholesale prices and prices from a day-ahead market. For instance,the local resource allocation market can be operated by a localdistribution network operator at the distribution feeder level. Further,the local distribution network operator can operate the local resourceallocation market as a capacity management market as described above inSection III.L. The local distribution network operator can receivewholesale price information and day-ahead prices from a regionaldistributor, which itself is a supplier to the local distributionnetwork operator (and may be the main or only supplier to the localdistribution feeder operator by the local distribution operator). Thelocal distribution operator may include one or more markups to thewholesale price or to the day-ahead price (e.g., a linear markup and/ora flat markup) before transmitting the price information to thetransactive and active controllers in the local resource allocationmarket. In certain implementations, the local distribution operatornetwork communicates with the resource consumers, resource suppliers,and the regional distributor using a network topology such as the oneshown in FIG. 2.

In the examples described below, the wholesale price information iscleared at 5 minute intervals, though any interval could be used. Also,in the examples, the day-ahead price information is cleared at 1 hourintervals, though any interval could be used.

The following parameters are used in the examples below. W is thewholesale price of electricity distributed to the local distributionfeeder (e.g., from a regional distributor). In the examples describedbelow, the wholesale price information is assumed to be determined at 5minute intervals, though any interval could be used. RTP_(W) is thereal-time price transmitted to customers. In the examples describedbelow, RTP_(W)=a(W)+b where a and b are markup parameters. As shown, ais a linear parameter and b is a flat parameter. RTP_(W) is the averageof the real-time price RTP_(W). In the examples described below, theaverage RTP_(W) is determined from the real-time prices RTP_(W) from arolling window comprising the previous 3 hours (a 3-hour rolling windowof 36 values). Although a rolling window of 3 hours is used, a rollingwindow of any duration could be used. σ_(W) is the standard deviation ofthe real-time price RTP_(W) from the window used to determine theaverage RTP_(W) . DA is the day-ahead price. In the examples describedbelow, the day-ahead price RTP_(DA) is the day-ahead price for thecurrent hour but one day ahead and is determined at one-hour intervals,though any interval could be used. Further, any futures prices could beused (e.g., two-day-ahead prices, week-ahead, or any other cleared pricefor future delivery of electricity). RTP_(DA) is the real-time day-aheadprice transmitted to customers. In the examples described below,RTP_(DA)=a′(DA)+b′ where a′ and b′ are markup parameters. As shown, a′is a linear parameter and b′ is a flat parameter. RTP_(DA) is theaverage of the day-ahead price RTP_(DA). In the examples describedbelow, the average RTP_(DA) is determined from the day-ahead pricesRTP_(DA) from a fixed or blocked window comprising a 24-hour window forthe next day (a 24-hour window of 24 values that transitions at aspecific time each day (e.g., midnight)). Although a fixed window of 24hours is used, a fixed or rolling window of any duration could be used.σ_(DA) is the standard deviation of the day-ahead prices RTP_(DA) fromthe window used to determine the average RTP_(DA) .

Furthermore, V_(actual) is the number of standard deviations of thecurrent price from the given expected price for a given pricing scheme,examples of which are described in the subsections below. Depending onthe pricing scheme, and as more fully explained below, either RTP_(W) ,RTP_(DA) , or both RTP_(W) and RTP_(DA) are used as the average (orexpected) price P_(average) in Equations (7)-(11) above (or otherbidding formula or control formula); either RTP_(W), RTP_(DA), or bothRTP_(W) and RTP_(DA) are used as the cleared price P_(clear) inEquations (7)-(11) above (or other bidding formula or control formula);and either σ_(DA), σ_(W), or both σ_(DA) and σ_(W) are used as thestandard deviation value of σ_(actual) in Equations (7)-(11) (or otherbidding formula or control formula).

A. Pricing Scheme Using Day-Ahead Prices

In one embodiment, the values P_(clear), P_(average), and σ_(actual)used in Equations (7)-(11) above (or other bidding formula or controlformula) are based on the day-ahead prices DA. In one exemplaryimplementation, RTP_(DA) is used as the cleared price P_(clear),RTP_(DA) is used as the average price P_(average), and σ_(DA) is used asthe standard deviation σ_(actual).

Thus, in one exemplary implementation of this embodiment, the bid pricecomputation of Equation (7) becomes:

$\begin{matrix}{P_{bid} = {\overset{\_}{{RTP}_{DA}} + {\left( {T_{current} - T_{set}} \right){\frac{k_{T} \times \sigma_{DA}}{{T_{limit} - T_{set}}}.}}}} & (12)\end{matrix}$

The adjusted zone set point computation of Equation (8) becomes:

$\begin{matrix}{T_{{set},a} = {T_{set} + {\left( {{RTP}_{DA} - \overset{\_}{{RTP}_{DA}}} \right){\frac{{T_{limit} - T_{set}}}{k_{T} \times \sigma_{DA}}.}}}} & (13)\end{matrix}$

Further, this equation can be rewritten as:

$\begin{matrix}{{T_{{set},a} = {T_{set} + {\upsilon_{actual}\frac{{T_{limit} - T_{set}}}{k_{T}}}}},} & (14)\end{matrix}$where υ_(actual) is computed as follows:

$\begin{matrix}{\upsilon_{actual} = \frac{{RTP}_{DA} - \overset{\_}{{RTP}_{DA}}}{\sigma_{DA}}} & (15)\end{matrix}$

Additionally, the computation of Equation (9) for the probabilityparameter used with one-way active controllers becomes:

$\begin{matrix}{{{r = {{k_{W}\left\lbrack {{\frac{1}{\sqrt{2\pi}\sigma_{DA}}{\int_{- \infty}^{{RTP}_{DA}}{{\mathbb{e}}^{\frac{{({\overset{\_}{{RTP}_{DA}} - x})}^{2}}{2\sigma_{DA}^{2}}}\ {\mathbb{d}x}}}} - \frac{1}{2}} \right\rbrack} = {k_{W}\left\lbrack {{N\left( {{RTP}_{DA},\overset{\_}{{RTP}_{DA}},\sigma_{DA}} \right)} - 0.5} \right\rbrack}}};{r \geq 0}}{{r = 0};{otherwise}}} & (16)\end{matrix}$

The electric vehicle charger bid price computation of Equation (10)becomes:P _(bid)=RTP_(DA) −kσ _(DA)SOC_(dev).  (17)

Further, the electric vehicle rate of charge computation of Equation(11) and used for one-way active controllers becomes:

$\begin{matrix}{{{ROC}_{set} = {{{ROC}_{des}\left( {1 - {k\left( \frac{{RTP}_{DA} - \overset{\_}{{RTP}_{DA}}}{\sigma_{DA}} \right)}} \right)} = {{ROC}_{des}\left( {1 - {k\;\upsilon_{actual}}} \right)}}},} & (18)\end{matrix}$where υ_(actual) is computed as in Equation (15).

In particular implementations, the values RTP_(DA), RTP_(DA) and σ_(DA)are updated according to the interval at which the day-ahead prices areupdated (e.g., one hour intervals). In some implementations, the localresource allocation market uses the price RTP_(DA) or a linearextrapolation of RTP_(DA) (e.g., RTP_(DA)(a)+b) as the posted marketclearing price P_(clear). In other implementations, only the valueσ_(actual) is based on the day-ahead prices DA (by using σ_(DA)).

B. Pricing Scheme Using Blend of Day-Ahead Prices and Wholesale Prices

In another embodiment, the cleared price P_(clear) used in Equations(7)-(11) above (or other bidding formula or control formula) is based onthe wholesale price whereas the average price P_(average) and thestandard deviation value σ_(actual) is based on day-ahead prices DA. Inone exemplary implementation, RTP_(W) is used as the cleared priceP_(clear), RTP_(DA) is used as the average price P_(average), and σ_(DA)is used as the standard deviation σ_(actual).

Thus, in one exemplary implementation of this embodiment, the bid pricecomputation of Equation (7) becomes:

$\begin{matrix}{P_{bid} = {\overset{\_}{{RTP}_{DA}} + {\left( {T_{current} - T_{set}} \right){\frac{k_{T} \times \sigma_{DA}}{{T_{limit} - T_{set}}}.}}}} & (19)\end{matrix}$

The adjusted zone set point computation of Equation (8) becomes:

$\begin{matrix}{T_{{set},a} = {T_{set} + {\left( {{RTP}_{W} - \overset{\_}{{RTP}_{DA}}} \right){\frac{{T_{limit} - T_{set}}}{k_{T} \times \sigma_{DA}}.}}}} & (20)\end{matrix}$

Further, this equation can be rewritten as:

$\begin{matrix}{{T_{{set},a} = {T_{set} + {\upsilon_{actual}\frac{{T_{limit} - T_{set}}}{k_{T}}}}},} & (21)\end{matrix}$where υ_(actual) is computed as follows:

$\begin{matrix}{\upsilon_{actual} = \frac{{RTP}_{W} - \overset{\_}{{RTP}_{DA}}}{\sigma_{DA}}} & (22)\end{matrix}$

Additionally, the computation of Equation (9) for the probabilityparameter used with one-way active controllers becomes:

$\begin{matrix}{\begin{matrix}{r = {k_{W}\left\lbrack {{\frac{1}{\sqrt{2\pi}\sigma_{DA}}{\int_{- \infty}^{{RTP}_{W}}{{\mathbb{e}}^{- \frac{{({\overset{\_}{{RTP}_{DA}} - x})}^{2}}{2\sigma_{DA}^{2}}}\ {\mathbb{d}x}}}} - \frac{1}{2}} \right\rbrack}} \\{{= {k_{W}\left\lbrack {{N\left( {{RTP}_{W},\overset{\_}{{RTP}_{DA}},\sigma_{DA}} \right)} - 0.5} \right\rbrack}};}\end{matrix}{r \geq 0}{{r = 0};{otherwise}}} & (23)\end{matrix}$

The electric vehicle charger bid price computation of Equation (10)becomes:P _(bid)=RTP_(W) −kσ _(DA)SOC_(dev).  (24)

Further, the electric vehicle rate of charge computation of Equation(11) and used for one-way active controllers becomes:

$\begin{matrix}\begin{matrix}{{ROC}_{set} = {{ROC}_{des}\left( {1 - {k\left( \frac{{RTP}_{W} - \overset{\_}{{RTP}_{DA}}}{\sigma_{DA}} \right)}} \right)}} \\{= {{ROC}_{des}\left( {1 - {k\;\upsilon_{actual}}} \right)}}\end{matrix} & (25)\end{matrix}$In particular implementations, the values RTP_(W), RTP_(DA) and σ_(DA)are updated according to the interval at which the wholesale prices areupdated (e.g., five-minute intervals). In some implementations, only thevalue σ_(actual) is computed as above (by using σ_(DA)).

C. Pricing Scheme Using Wholesale Prices

In another embodiment, the values P_(clear), P_(average), and σ_(actual)used in Equations (7)-(11) above (or other bidding formula or controlformula) are based only on the wholesale prices W. In one exemplaryimplementation, RTP_(W) is used as the cleared price P_(clear), RTP_(W)is used as the average price P_(average), and the value σ_(W) is used asthe standard deviation σ_(actual).

Thus, in one exemplary implementation of this embodiment, the bid pricecomputation of Equation (7) becomes:

$\begin{matrix}{P_{bid} = {\overset{\_}{{RTP}_{W}} + {\left( {T_{current} - T_{set}} \right){\frac{k_{T} \times \sigma_{W}}{{T_{limit} - T_{set}}}.}}}} & (26)\end{matrix}$

The adjusted zone set point computation of Equation (8) becomes:

$\begin{matrix}{T_{{set},a} = {T_{set} + {\left( {{RTP}_{W} - \overset{\_}{{RTP}_{W}}} \right){\frac{{T_{limit} - T_{set}}}{k_{T} \times \sigma_{W}}.}}}} & (27)\end{matrix}$

Further, this equation can be rewritten as:

$\begin{matrix}{{T_{{set},a} = {T_{set} + {\upsilon_{actual}\frac{{T_{limit} - T_{set}}}{k_{T}}}}},} & (28)\end{matrix}$where υ_(actual) is computed as follows:

$\begin{matrix}{\upsilon_{actual} = \frac{{RTP}_{W} - \overset{\_}{{RTP}_{W}}}{\sigma_{W}}} & (29)\end{matrix}$

Additionally, the computation of Equation (9) for the probabilityparameter used with one-way active controllers becomes:

$\begin{matrix}{\begin{matrix}{r = {k_{W}\left\lbrack {{\frac{1}{\sqrt{2\pi}\sigma_{W}}{\int_{- \infty}^{{RTP}_{W}}{{\mathbb{e}}^{- \frac{{({\overset{\_}{{RTP}_{W}} - x})}^{2}}{2\sigma_{W}^{2}}}\ {\mathbb{d}x}}}} - \frac{1}{2}} \right\rbrack}} \\{{= {k_{W}\left\lbrack {{N\left( {{RTP}_{W},\overset{\_}{{RTP}_{W}},\sigma_{W}} \right)} - 0.5} \right\rbrack}};}\end{matrix}{r \geq 0}{{r = 0};{otherwise}}} & (30)\end{matrix}$

The electric vehicle charger bid price computation of Equation (10)becomes:P=RTP_(W) −kσ _(W)SOC_(dev).  (31)

Further, the electric vehicle rate of charge computation of Equation(11) and used for one-way active controllers becomes:

$\begin{matrix}\begin{matrix}{{ROC}_{set} = {{ROC}_{des}\left( {1 - {k\left( \frac{{RTP}_{W} - \overset{\_}{{RTP}_{W}}}{\sigma_{W}} \right)}} \right)}} \\{= {{ROC}_{des}\left( {1 - {k\;\upsilon_{actual}}} \right)}}\end{matrix} & (32)\end{matrix}$In particular implementations, the values RTP_(W), RTP_(W) , and σ_(W)are updated according to the interval at which the wholesale prices areupdated (e.g., five-minute intervals). In some implementations, only thevalue σ_(actual) is computed as above (by using σ_(W)).

D. Pricing Scheme Using Weighted Contribution of Wholesale Prices andDay-Ahead Prices

In another embodiment, the values P_(clear), P_(average), and σ_(actual)used in Equations (7)-(11) above (or other bidding formula or controlformula) are based on a weighted sum that allows both the wholesaleprices W and the day-ahead prices DA to contribute.

In one particular embodiment, the local resource allocation market usesthe following as the posted market clearing price P_(clear):P _(clear) =a(RTP_(W))+(1−a)(RTP_(DA)).  (33)Further, in this embodiment, the local resource allocation market usesthe following as the price P_(average):P _(average) =a( RTP_(W) )+(1−a)( RTP_(DA) ).  (34)Additionally, in this embodiment, the local resource allocation marketuses the following as the standard deviation σ_(actual):σ_(actual)=√{square root over (a ²σ_(W)²+2a(1−a)σ_(W)σ_(DA)+(1−a)²σ_(DA) ²)}{square root over (a ²σ_(W)²+2a(1−a)σ_(W)σ_(DA)+(1−a)²σ_(DA) ²)}  (35)In Equations (33) through (35) (as well as the equations below), aspecifies a weighting fraction of the wholesale price versus theday-ahead price and comprises a value between 0 and 1.

Thus, in one exemplary implementation of this embodiment, the bid pricecomputation of Equation (7) becomes:

$\begin{matrix}{{P_{bid} = {\left( {{a\left( \overset{\_}{{RTP}_{W}} \right)} + {\left( {1 - a} \right)\left( \overset{\_}{{RTP}_{DA}} \right)}} \right) + {\left( {T_{current} - T_{set}} \right)\frac{k_{T} \times \sigma_{actual}}{{T_{limit} - T_{set}}}}}},} & (36)\end{matrix}$where σ_(actual) is computed as in Equation (35).

The adjusted zone set point computation of Equation (8) becomes:

$\begin{matrix}{{T_{{set},a} = {T_{set} + {\left( {\left( {{a\left( {RTP}_{W} \right)} + {\left( {1 - a} \right)\left( {RTP}_{DA} \right)}} \right) - \left( {{a\left( \overset{\_}{{RTP}_{W}} \right)} + {\left( {1 - a} \right)\left( \overset{\_}{{RTP}_{DA}} \right)}} \right)} \right)\frac{{T_{limit} - T_{set}}}{k_{T} \times \sigma_{W}}}}},} & (37)\end{matrix}$where σ_(actual) is computed as in Equation (35). Further, Equation (37)can be rewritten as:

$\begin{matrix}{{T_{{set},a} = {T_{set} + {\upsilon_{actual}\frac{{T_{limit} - T_{set}}}{k_{T}}}}},} & (38)\end{matrix}$where υ_(actual) is computed as follows:

$\begin{matrix}{\upsilon_{actual} = \frac{{a\left( {{RTP}_{W} - \overset{\_}{{RTP}_{W}}} \right)} + {\left( {1 - a} \right)\left( {{RTP}_{DA} - \overset{\_}{{RTP}_{DA}}} \right)}}{\sqrt{{a^{2}\sigma_{W}^{2}} + {2{a\left( {1 - a} \right)}\sigma_{W}\sigma_{DA}} + {\left( {1 - a} \right)^{2}\sigma_{DA}^{2}}}}} & (39)\end{matrix}$

Additionally, the computation of Equation (9) for the probabilityparameter used with one-way active controllers becomes:

$\begin{matrix}{\begin{matrix}{r = {k_{W}\left\lbrack {{\frac{1}{\sqrt{2\pi}\sigma_{actual}}{\int_{- \infty}^{{a{({RTP}_{W})}} + {{({1 - a})}{({RTP}_{DA})}}}{{\mathbb{e}}^{- \frac{{({{({{a{(\overset{\_}{{RTP}_{W}})}} + {{({1 - a})}{(\overset{\_}{{RTP}_{DA}})}}})} - x})}^{2}}{2\sigma_{actual}^{2}}}\ {\mathbb{d}x}}}} - \frac{1}{2}} \right\rbrack}} \\{{= {k_{W}\left\lbrack {{N\left( {\left( {{a\left( {RTP}_{W} \right)} + {\left( {1 - a} \right)\left( {RTP}_{DA} \right)}} \right),\left( {{a\left( \overset{\_}{{RTP}_{W}} \right)} + {\left( {1 - a} \right)\left( \overset{\_}{{RTP}_{DA}} \right)}} \right),\sigma_{actual}} \right)} - 0.5} \right\rbrack}};}\end{matrix}{r \geq 0}{{r = 0};{otherwise}}} & (40)\end{matrix}$

The electric vehicle charger bid price computation of Equation (10)becomes:P _(bid)=(a(RTP_(W))+(1−a)(RTP_(DA)))−kσ _(actual)SOC_(dev),  (41)where σ_(actual) is computed as in Equation (35).

Further, the electric vehicle rate of charge computation of Equation(11) and used for one-way active controllers becomes:

$\begin{matrix}\begin{matrix}{{ROC}_{set} = {{ROC}_{des}\left( {1 - {k\left( \frac{\begin{matrix}{{a\left( {{RTP}_{W} - \overset{\_}{{RTP}_{W}}} \right)} +} \\{\left( {1 - a} \right)\left( {{RTP}_{DA} - \overset{\_}{{RTP}_{DA}}} \right)}\end{matrix}}{\sqrt{{a^{2}\sigma_{W}^{2}} + {2{a\left( {1 - a} \right)}\sigma_{W}\sigma_{DA}} + {\left( {1 - a} \right)^{2}\sigma_{DA}^{2}}}} \right)}} \right)}} \\{= {{ROC}_{des}\left( {1 - {k\;\upsilon_{actual}}} \right)}}\end{matrix} & (42)\end{matrix}$

Further, in certain implementations, the components of Equation(33)-(42) are updated at different intervals. For instance, thecomponents related to real-time wholesale prices W can be updated on5-minute intervals and the components related to day-ahead prices DA canbe updated on one-hour intervals. In some implementations, only thestandard deviation σ_(actual) is computed as above (by using √{squareroot over (a²σ_(W) ²+2a(1−a)σ_(W)σ_(DA)+(1−a)²σ_(DA) ²)}{square rootover (a²σ_(W) ²+2a(1−a)σ_(W)σ_(DA)+(1−a)²σ_(DA) ²)}).

In some implementations, the value of a is not constant, but varies. Forexample, the value of a can change depending on a current state of thelocal distribution feeder. For instance, as the local distributionfeeder approaches capacity (e.g., during critical or peak periods oftime), the weighting function a can change so that the real-time pricesare favored (e.g., the influence of the real-time prices become themajor contributor to Equation (9)), thus making the controllers moresensitive to volatility in the market and responsive to sudden pricechanges that might occur as a result of the capacity of the feeder beingreached. Similarly, the weighting function a can change so that theday-ahead prices are favored during off-critical or non-peak periods oftime (e.g., the influence of the day-ahead prices become the majorcontributor to Equation (9)), thus making the controllers less sensitiveto volatility in the market. In some instances, the weighting value aswitches between relatively extreme values (e.g., 0 and 1), effectivelyoperating as a switch between two or more different pricing schemes.

VI. Concluding Remarks

Having described and illustrated the principles of the disclosedtechnology in the detailed description and accompanying drawings, itwill be recognized that the various embodiments can be modified inarrangement and detail without departing from such principles.

For example, it is to be understood that any of the features andembodiments described herein can be used in combination with any of thefeatures and embodiments described in U.S. Provisional Application No.61/194,596 filed on Sep. 29, 2008, and entitled “METHOD AND SYSTEM FORELECTRIC POWER GRID CONTROL”; U.S. Nonprovisional application Ser. No.12/587,008, filed on Sep. 29, 2009, and entitled “ELECTRIC POWER GRIDCONTROL USING A MARKET-BASED RESOURCE ALLOCATION SYSTEM” (published asU.S. Patent Application Publication No. 2010/0114387); U.S.Nonprovisional application Ser. No. 12/587,009, filed on Sep. 29, 2009,and entitled “USING BI-DIRECTIONAL COMMUNICATIONS IN A MARKET-BASEDRESOURCE ALLOCATION SYSTEM” (published as U.S. Patent ApplicationPublication No. 2010/0106332); U.S. Nonprovisional application Ser. No.12/587,006, filed on Sep. 29, 2009, and entitled “USING ONE-WAYCOMMUNICATIONS IN A MARKET-BASED RESOURCE ALLOCATION SYSTEM” (publishedas U.S. Patent Application Publication No. 2010/0106641); U.S.Nonprovisional application Ser. No. 12/587,000, filed on Sep. 29, 2009,and entitled “DISTRIBUTING RESOURCES IN A MARKET-BASED RESOURCEALLOCATION SYSTEM” (published as U.S. Patent Application Publication No.2010/0107173); U.S. Provisional Application No. 61/143,954 filed on Jan.12, 2009, and entitled “NESTED, HIERARCHICAL RESOURCE ALLOCATION SCHEMAFOR MANAGEMENT AND CONTROL OF AN ELECTRIC POWER GRID”; U.S.Nonprovisional application Ser. No. 12/686,243, filed on Jan. 12, 2010,and entitled “NESTED, HIERARCHICAL RESOURCE ALLOCATION SCHEMA FORMANAGEMENT AND CONTROL OF AN ELECTRIC POWER GRID,” (published as U.S.Patent Application Publication No. 2010/0179862); and U.S.Nonprovisional application Ser. No. 13/096,770, filed on Apr. 28, 2011concurrently herewith, and entitled “PREVENTING CONFLICTS AMONG BIDCURVES USED WITH TRANSACTIVE CONTROLLERS IN A MARKET-BASED RESOURCEALLOCATION SYSTEM,” all of which are hereby incorporated herein byreference.

In view of the many possible embodiments to which the principles of thedisclosed invention may be applied, it should be recognized that theillustrated embodiments are only preferred examples of the invention andshould not be taken as limiting the scope of the invention. Rather, thescope of the invention is defined by the following claims and theirequivalents. We therefore claim as our invention all that comes withinthe scope and spirit of these claims.

What is claimed is:
 1. A method for operating an electrical device in amarket-based resource allocation system, the method comprising:receiving, by computing hardware associated with the electrical device,a user comfort setting selected by a user, the user comfort settingbeing selected from at least a first user comfort setting and a seconduser comfort setting, the first user comfort setting indicating theuser's willingness to pay more to achieve a desired performance levelfor the electrical device relative to the second user comfort setting;receiving, by the computing hardware, a cleared price for electricityfrom a local resource allocation market from which the electrical devicereceives electricity; receiving, by the computing hardware, priceinformation from an electricity futures market; computing, by thecomputing hardware, a probability value of operating the electricaldevice based at least in part on the user comfort setting, the clearedprice for electricity from the local resource allocation market, and theprice information from the electricity futures market; generating, bythe computing hardware, a random number; determining, by the computinghardware, whether to operate the electrical device by comparing therandom number to the probability value; and activating or deactivating,by the computing hardware, the electrical device based on the comparisonbetween the random number and the probability value.
 2. The method ofclaim 1, wherein the electricity futures market is a day-aheadelectricity market.
 3. The method of claim 1, wherein the priceinformation from the electricity futures market comprises cleared pricesfrom the electricity futures market, and wherein the computing of theprobability value is performed using an average of the cleared pricesfrom the electricity futures market and a standard deviation of thecleared prices from the electricity futures market.
 4. The method ofclaim 3, further comprising computing the average and the standarddeviation with the computing hardware or receiving the average and thestandard deviation from a remote source.
 5. The method of claim 1,wherein the computing of the probability value is performed based atleast in part on the price information from the electricity futuresmarket and at least in part on price information from the local resourceallocation market.
 6. The method of claim 5, wherein the computing ofthe probability value is performed based at least in part on one or moreweighted sums computed from the price information from the electricityfutures market and from the price information for the local resourceallocation market.
 7. The method of claim 6, wherein one or more of theweighted sums is controlled by a variable weighting factor.
 8. Themethod of claim 5, wherein the price information from the electricityfutures market comprises price information from a fixed window of timefrom a day-ahead electricity market, and wherein the price informationfrom the local resource allocation market comprises price informationfor a rolling window of time.
 9. The method of claim 1, wherein theelectrical device is one of an air-conditioning unit, heating unit, hotwater heater, refrigerator, dish washer, washing machine, dryer, oven,microwave oven, pump, home lighting system, electric vehicle charger, orhome electrical system.
 10. One or more non-transitory computer-readablemedia storing computer-executable instructions for causing a computer toperform a method for operating an electrical device in a market-basedresource allocation system, the method comprising: receiving a usercomfort setting selected by a user, the user comfort setting beingselected from at least a first user comfort setting and a second usercomfort setting, the first user comfort setting indicating the user'swillingness to pay more to achieve a desired performance level for theelectrical device relative to the second user comfort setting; receivinga cleared price for electricity from a local resource allocation marketfrom which the electrical device receives electricity; receiving priceinformation from an electricity futures market; computing a probabilityvalue of operating the electrical device based at least in part on theuser comfort setting, the cleared price for electricity from the localresource allocation market, and the price information from theelectricity futures market; generating a random number; determiningwhether to operate the electrical device by comparing the random numberto the probability value; and activating or deactivating the electricaldevice based on the comparison between the random number and theprobability value.
 11. A system comprising memory and a processorprogrammed to operate an electrical device in a market-based resourceallocation system, the processor programmed to: receive a user comfortsetting selected by a user, the user comfort setting being selected fromat least a first user comfort setting and a second user comfort setting,the first user comfort setting indicating the user's willingness to paymore to achieve a desired performance level for the electrical devicerelative to the second user comfort setting; receive a cleared price forelectricity from a local resource allocation market from which theelectrical device receives electricity; receive price information froman electricity futures market; compute a probability value of operatingthe electrical device based at least in part on the user comfortsetting, the cleared price for electricity from the local resourceallocation market, and the price information from the electricityfutures market; generate a random number; determine whether to operatethe electrical device by comparing the random number to the probabilityvalue; and activate or deactivate the electrical device based on thecomparison between the random number and the probability value.
 12. Theone or more non-transitory computer-readable media of claim 10, whereinthe electricity futures market is a day-ahead electricity market. 13.The one or more non-transitory computer-readable media of claim 10,wherein the price information from the electricity futures marketcomprises cleared prices from the electricity futures market, andwherein the computing of the probability value is performed using anaverage of the cleared prices from the electricity futures market and astandard deviation of the cleared prices from the electricity futuresmarket.
 14. The one or more non-transitory computer-readable media ofclaim 13, wherein the method further comprises computing the average andthe standard deviation with the computing hardware or receiving theaverage and the standard deviation from a remote source.
 15. The one ormore non-transitory computer-readable media of claim 10, wherein thecomputing of the probability value is performed based at least in parton the price information from the electricity futures market and atleast in part on price information from the local resource allocationmarket.
 16. The one or more non-transitory computer-readable media ofclaim 15, wherein the computing of the probability value is performedbased at least in part on one or more weighted sums computed from theprice information from the electricity futures market and from the priceinformation for the local resource allocation market.
 17. The one ormore non-transitory computer-readable media of claim 16, wherein one ormore of the weighted sums is controlled by a variable weighting factor.18. The one or more non-transitory computer-readable media of claim 15,wherein the price information from the electricity futures marketcomprises price information from a fixed window of time from a day-aheadelectricity market, and wherein the price information from the localresource allocation market comprises price information for a rollingwindow of time.
 19. The one or more non-transitory computer-readablemedia of claim 10, wherein the electrical device is one of anair-conditioning unit, heating unit, hot water heater, refrigerator,dish washer, washing machine, dryer, oven, microwave oven, pump, homelighting system, electric vehicle charger, or home electrical system.